The DOI matches its Crossref record; 34 of the 36 references it cites were found in a registry or an index; none of the 27 sources its claims cite is checked for support yet.
Weighted boxes fusion: Ensembling boxes from different object detection models
Open accessOverview
AI snapshot
Written from the paper’s full text.
TL;DR
Weighted Boxes Fusion (WBF) ensembles object detection boxes by fusing all predictions using confidence-weighted coordinates and rescaling confidence by cluster size and model count. It outperformed NMS, soft-NMS, and NMW on COCO and Open Images ensembles, reaching 56.1 mAP on COCO validation and 56.4 mAP on COCO test.
Key findings
- On a two-model COCO ensemble (EfficientDetB6 + EfficientDetB7), WBF achieved mAP(0.5..0.95) = 0.5344, mAP@0.5 = 0.7244, and mAP@0.75 = 0.5824, compared with NMS 0.5269/0.7156/0.5737, soft-NMS 0.5239/0.7121/0.5677, and NMW 0.5285/0.7171/0.5743.
- For test-time augmentation with EfficientDetB7, WBF reached mAP(0.5..0.95) = 0.5262, versus NMS 0.5233, soft-NMS 0.5210, and NMW 0.5250.
- On a many-model COCO ensemble, WBF gave 56.1 mAP on validation and 56.4 mAP on test, described as the top third result on the MS COCO leaderboard as of 07/08/2020.
- On Open Images, WBF reached 0.5665 mAP@0.5 for a RetinaNet ensemble (vs NMS 0.5442 and soft-NMS 0.5428) and 0.5982 mAP@0.5 for an ensemble of fairly different models (vs NMW 0.5667, NMS 0.5642, soft-NMS 0.5616), helping achieve 7th out of 558 in the challenge.
Methods
- WBF collects all predicted boxes, sorts them by confidence, clusters boxes with IoU > THR (THR = 0.55 was close to optimal in experiments), fuses coordinates as confidence-weighted sums, sets fused confidence as the average confidence, then rescales confidence by the number of boxes in the cluster divided by the number of models.
- Evaluation used Open Images V5 (about 16M boxes, 600 classes, 1.9M images; training 1,743,042 images, validation 41,620, test 99,999; mAP@0.5) and MS COCO (over 200,000 images, 80 categories; train2017 118k, val2017 5k, test 20k; mAP over IoU 0.5 to 0.95).
- Models included RetinaNet, EfficientDet, DetectoRS, YOLOv5, Faster R-CNN, Mask R-CNN, and Cascade R-CNN; WBF was compared against NMS, soft-NMS, and NMW, with grid search for optimal parameters.
Limitations
- The paper states WBF is slower than NMS and soft-NMS, on average around three times slower than standard NMS.
- The paper reports that replacing NMS at the output of an individual model with WBF degraded performance: RetinaNet ResNet152 on Open Images gave raw boxes 0.1718 mAP, NMS default 0.4902, NMS optimal 0.4906, and WBF optimal 0.4532; the authors attribute this to excessive low-scored wrong predictions, noting WBF works better for ensembles of fairly accurate models.
- Evident from the text: WBF performance depends on tuned parameters such as IoU threshold and model weights, which were optimized by grid search in the reported experiments.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
16 Not checked yet · 15 Own result · 8 Own interpretation · 5 No basis given · 3 Own method
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This method significantly improves the quality of the combined predicted rectangles.”
- Also stated in: § 6.2. An ensemble of two different models · p. 4 · boosted
“Even for the ensemble of only two models, the WBF method outperformed significantly other methods.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § 6.2. An ensemble of two different models · p. 4 · boosted
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the WBF method demonstrated the best results.”
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The third top entry represents the results described in this paper.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The WBF method outperformed by far the other algorithms (see Table 5)”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Combining predictions from several detectors with different backbones yields better performance”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The technique was evaluated in the context of the Open Images Object Detection Challenge and helped achieving one of the top results in the challenge [3].”
- Also stated in: § Abstract · p. 1 · plain
“We tested method on several datasets and evaluated it in the context of the Open Images and COCO Object Detection tracks, achieving top results in these challenges.”
What the sources say
- Not checked yetcites [3]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Abstract · p. 1 · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“NMS with default IoU threshold = 0.5 (e.g. standard model output) - mAP: 0.4902”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Raw boxes (no NMS/WBF) - mAP: 0.1718”
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The method was expanded on the 3D boxes fusion as well.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“WBFwith optimal parameters - mAP: 0.4532 (the optimized parameters: IoU threshold = 0.43, skip threshold = 0.21)”
plaincorrelationalSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 13
- Probably not a substantial claim. 15
- The quote only partly states this claim. 3
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The WBF method for combining predictions of detection models gave 56.1 mAP for the validation data set and 56.4 mAP for the test set.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the top performance for the COCO Detection Challenge (( 56.1 mAP for validation data set and 56.4 mAP for the test-dev set).”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Note: in our experiments, THR = 0 . 55 was close to an optimal threshold.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Even for the ensemble of only two models, the WBF method outperformed significantly other methods.”
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the WBF algorithm, on average, is around three times slower than the standard NMS.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“| NMS | 0.5233 | 0.7129 | 0.5656 |”
- Also stated in: § 6.3. Test-time-augmentation ensemble · p. 5 · plain
“| Soft-NMS | 0.5210 | 0.7092 | 0.5633 |”
- Also stated in: § 6.3. Test-time-augmentation ensemble · p. 5 · plain
“| NMW | 0.5250 | 0.7138 | 0.5691 |”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § 6.3. Test-time-augmentation ensemble · p. 5 · plain
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In practice, the outcome for both variants did not differ significantly, with the first being slightly better.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“It is a considerable improvement compared to the performance of individual models and gives the top third result on the MS COCO official leaderboard [6] (as of 07/08/2020).”
What the sources say
- Not checked yetcites [6]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“the winners from the first place of 3D Detection and Domain Adaptation track [13] and the second place from the 2D Object Detection track [11] used this method for combining predictions.”
What the sources say
- Not checked yetcites [13]
- Not checked yetcites [11]
- Not checked yetcites [19]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The soft-NMS method was proposed in [9] to reduce this problem. Instead of completely removing the detection proposals with high IoU and high confidence, it reduces the confidences of the proposals proportional to IoU value.”
- Also stated in: § 2.2. Soft-NMS · p. 2 · boosted
“Soft-NMS demonstrated noticeable improvements over traditional NMS on standard benchmark datasets, like PASCALVOCandMSCOCO[5]. It has shown to improve performance for single models [9].”
What the sources say
- Not checked yetcites [9]
- Not checked yetcites [5]
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 2.2. Soft-NMS · p. 2 · boosted
None of the sources cited for this claim have been checked yet.
In the paper
“Microsoft COCO [5, 23], one of the most popular benchmark datasets for object detection.”
What the sources say
- Not checked yetcites [5]
- Not checked yetcites [23]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Both NMS and soft-NMS exclude some boxes, while WBF uses all boxes. Thus, it can fix cases where all boxes are predicted inaccurately by all models. This case is illustrated in Figure3. NMS/soft-NMS will leave only one inaccurate box, while WBF will fuse it using all predicted boxes.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As the test set labels are not available, we performed the ablation study on the validation set.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: The test set labels are not available.
In the paper
“It was also reported that the WBF method gives the best results on the Wheat detection dataset for boxes ensemble comparing to other methods [32].”
What the sources say
- Not checked yetcites [32]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The first place winners from Lyft 3D Object Detection for Autonomous Vehicles [19] reported that WBF gives better score for their ensemble (0.222 vs 0.220) [48].”
What the sources say
- Not checked yetcites [48]
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These results can be reproduced using a benchmark published on GitHub [46].”
What the sources say
- Not checked yetcites [46]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Combining predictions from different models generalizes better and usually yields more accurate results compared to a single model [27].”
What the sources say
- Not checked yetcites [27]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Such methods work well on a single model. However, they only select the boxes and can not produce averaged localization of predictions combined from various models effectively.”
- Also stated in: § 2.2. Soft-NMS · p. 2 · plain
“However, both NMS and soft-NMS discard redundant boxes, and thus can not produce averaged localization predictions from different models effectively.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 2.2. Soft-NMS · p. 2 · plain
In the paper
“These models have recently shown top results on the COCO database [36].”
What the sources say
- Not checked yetcites [36]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We think it is due to the excessive number of low scored wrong predictions given by the model output.”
hedgeddirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Rests on an assumption: Most neural networks for Object Detection use NMS or Soft-NMS output to filter predicted boxes.
None of the sources cited for this claim have been checked yet.
In the paper
“The non-maximum weighted (NMW) method proposed in [49, 26] has a similar idea. However, the NMW method does not change confidence scores; it uses IoU value to weight the boxes. NMW uses a box with the highest confidence to compare with, while WBF updates a fused box at each step, and uses it to check the overlap with the next predicted boxes.”
What the sources say
- Not checked yetcites [49]
- Not checked yetcites [26]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We used a grid search to find the optimal parameters for each of the four methods, which allowed achieving the best mAP for the given method.”
- Also stated in: § 6.3. Test-time-augmentation ensemble · p. 4 · plain
“Then, we perform a grid search to find optimal parameters for all four ensemble methods.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Also stated in: § 6.3. Test-time-augmentation ensemble · p. 4 · plain
None of the sources cited for this claim have been checked yet.
In the paper
“The ensemble methods often get top places in machine learning competitions, for example, see [12, 15, 34, 33, 42, 7].”
What the sources say
- Not checked yetcites [12]
- Not checked yetcites [15]
- Not checked yetcites [34]
- Not checked yetcites [33]
- Not checked yetcites [42]
- Not checked yetcites [7]
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Also, NMW does not use information about how many models predict a given box in a cluster and, therefore, does not produce the best results for models' ensemble.”
What the sources say
- Not checked yetcites [49]
- Not checked yetcites [26]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The NMS or soft-NMS produces more efficient filtering for those predictions”
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
- Rests on an assumption: Most neural networks for Object Detection use NMS or Soft-NMS output to filter predicted boxes.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“when it comes to a large number of overlapping boxes with different confidence scores, WBF gives worse results than NMS.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: Most neural networks for Object Detection use NMS or Soft-NMS output to filter predicted boxes.
In the paper
“Such errors reduce the precision of the model.”
What the sources say
- Not checked yetcites [25]
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The weights in the final ensemble and IoU threshold for the WBF methods were optimized using the validation set.”
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“These results can be reproduced using benchmark published on GitHub [46].”
What the sources say
- Not checked yetcites [46]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
The 3D version of boxes fusion was successfully applied by the winning teams of Waymo Open Dataset and Lyft 3D Object Detection for Autonomous Vehicles challenges.
“The 3D version of boxes fusion was successfully applied by the winning teams of Waymo Open Dataset and Lyft 3D Object Detection for Autonomous Vehicles challenges.”
The paper relies on this without support.
The Open Images dataset is the largest labeled dataset with object detection annotations to date.
“We used the Open Images data set [20], the largest data set with object detection annotations to date”
“To date, it is the largest labeled dataset with object detection annotations.”
The paper relies on this without support.
The test set labels are not available.
“As the test set labels are not available, we performed the ablation study on the validation set.”
The paper relies on this without support.
Most neural networks for Object Detection use NMS or Soft-NMS output to filter predicted boxes.
“Most neural networks for Object Detection use NMS or Soft-NMS output to filter predicted boxes.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The advantage of this type of method is that it takes full consideration of object relationships and their spatial layout [118, 119].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [118]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 3. Weighted Boxes Fusion · p. 3
“This case is illustrated in Figure3. NMS/soft-NMS will leave only one inaccurate box, while WBF will fuse it using all predicted boxes.”
§ 6.4.2 Ensemble of RetinaNet models for Open Images Dataset · p. 6
“Figure 3: The official leader boards of COCO Detection Challenge (Bounding Box) as of 07/08/20.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ 6.3. Test-time-augmentation ensemble · p. 5
“| Model | mAP(0.5..0.95) | mAP(@0.5 IoU) | mAP(@0.5 IoU) |”
§ 6.3. Test-time-augmentation ensemble · p. 5
“| Method | mAP(0.5..0.95) | mAP(0.5) | mAP(0.75) |”
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
Microsoft COCO: Common Objects in Context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., & Zitnick, C. L. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10602-1_48 · Cited by 45,460
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., & Urtasun, R. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913491297 · Cited by 9,913
EfficientDet: Scalable and Efficient Object Detection
Tan, M., Pang, R., & Le, Q. V. (2019) · arXiv · Cited by 9,841
Cascade R-CNN: Delving Into High Quality Object Detection
Cai, Z., & Vasconcelos, N. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00644 · Cited by 7,370
Cited by
Sorted by citations
Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689
“object relationships and their spatial layout [118], [119].”
Zhu, X., Lyu, S., Wang, X., & Zhao, Q. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00312 · Cited by 2,444
ViT-YOLO:Transformer-Based YOLO for Object Detection
Zhang, Z., Lu, X., Cao, G., Yang, Y., Jiao, L., & Liu, F. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00314 · Cited by 325
VisDrone-DET2021: The Vision Meets Drone Object detection Challenge Results
Cao, Y., He, Z., Wang, L., Wang, W., Yuan, Y., Zhang, D., Zhang, J., Zhu, P., Gool, L. V., Han, J., et al. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00319 · Cited by 314
Self-citationBouguettaya, A., Zarzour, H., Taberkit, A. M., & Kechida, A. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108309 · Cited by 318
Multimodal Object Detection via Probabilistic Ensembling
Chen, Y.-T., Shi, J., Ye, Z., Mertz, C., Ramanan, D., & Kong, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20077-9_9 · Cited by 252
A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5
Zhao, J., Zhang, X., Yan, J., Qiu, X., Yao, X., Tian, Y., Zhu, Y. P., & Cao, W. (2021) · Remote Sensing · doi:10.3390/rs13163095 · Cited by 206
YOLOv7-sea: Object Detection of Maritime UAV Images based on Improved YOLOv7
Zhao, H., Zhang, H., & Zhao, Y. (2023) · IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW) · doi:10.1109/wacvw58289.2023.00029 · Cited by 181
Offboard 3D Object Detection from Point Cloud Sequences
Qi, C. R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., & Anguelov, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00607 · Cited by 176
Delving Into the Devils of Bird’s-Eye-View Perception: A Review, Evaluation and Recipe
Li, H., Sima, C., Dai, J., Wang, W., Lu, L., Wang, H., Zeng, J., Li, Z., Yang, J., Deng, H., et al. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3333838 · Cited by 211
Self-citationDeep learning in diabetic foot ulcers detection: A comprehensive evaluation
Yap, M. H., Hachiuma, R., Alavi, A., Brüngel, R., Cassidy, B., Goyal, M., Zhu, H., Rückert, J., Olshansky, M., Huang, X., et al. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104596 · Cited by 192
Du, Y., Wan, J., Zhao, Y., Zhang, B., Tong, Z., & Dong, J. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00315 · Cited by 179
Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models
Hardalaç, F., Uysal, F., Peker, O., Çiçeklidağ, M., Tolunay, T., Tokgöz, N., Kutbay, U., Demirciler, B., & Mert, F. (2022) · Sensors · doi:10.3390/s22031285 · Cited by 162
Confidence-Aware Fusion Using Dempster-Shafer Theory for Multispectral Pedestrian Detection
Li, Q., Zhang, C., Hu, Q., Fu, H., & Zhu, P. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3160589 · Cited by 132
Zhao, Q., Liu, B., Lyu, S., Wang, C., & Zhang, H. (2023) · Remote Sensing · doi:10.3390/rs15061687 · Cited by 127
Detecting Everything in the Open World: Towards Universal Object Detection
Wang, Z., Li, Y., Chen, X., Lim, S.-N., Torralba, A. B., Zhao, H., & Wang, S. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01100 · Cited by 111
Bakırcı, M. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104594 · Cited by 98
Xiong, X., Meng, A., Lu, J., Tan, Y., Chen, B., Tang, J., Zhang, C., Xiao, S., & Hu, J. (2023) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2023.134483 · Cited by 101
An X-Ray-Based Automatic Welding Defect Detection Method for Special Equipment System
Zuo, F., Liu, J., Xiang, Z., Chen, L., & Wang, L. (2023) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2023.3327713 · Cited by 76
Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection
Yu, K., Tang, T., Xie, H., Lin, Z., Liang, T., Wang, B., Chen, P., Hao, D., Wang, Y., & Liang, X. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00321 · Cited by 86
Maize tassel detection with CA-YOLO for UAV images in complex field environments
Jia, Y., Fu, K., Lan, H., Wang, X., & Su, Z. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2023.108562 · Cited by 69
Multi-modal remote perception learning for object sensory data
Almujally, N. A., Rafique, A. A., Mudawi, N. A., Alazeb, A., Alonazi, M., Algarni, A. M., Jalal, A., & Liu, H. (2024) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2024.1427786 · Cited by 66
Diabetic Foot Ulcer Detection: Combining Deep Learning Models for Improved Localization
Sarmun, R., Chowdhury, M. E. H., Murugappan, M., Aqel, A., Ezzuddin, M., Rahman, S. M., Khandakar, A. A., Akter, S., Alfkey, R., & Hasan, A. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10267-3 · Cited by 68
Combining transformer and CNN for object detection in UAV imagery
Hendria, W. F., Phan, Q. T., Adzaka, F., & Jeong, C. (2021) · ICT Express · doi:10.1016/j.icte.2021.12.006 · Cited by 66
Context and scale-aware YOLO for welding defect detection
Kwon, J. E., Park, J. H., Kim, J. H., Lee, Y. H., & Cho, S. I. (2023) · NDT & E International · doi:10.1016/j.ndteint.2023.102919 · Cited by 69
Bag of Strategies Set New State-of-the-art for Algae Object Detectors
Yang, Z., Wen, H., Wei, Z., & Zhang, Z. (2022) · International Conference on Universal Village (UV) · doi:10.1109/uv56588.2022.10185474 · Cited by 57
Aerial Person Detection for Search an d Rescue: Survey and Benchmarks
Zhang, X., Feng, Y., Wang, N., Lü, G., & Mei, S. (2025) · Journal of Remote Sensing · doi:10.34133/remotesensing.0474 · Cited by 57
Malawade, A. V., Mortlock, T., & Faruque, M. A. A. (2022) · ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) · doi:10.1109/iccps54341.2022.00013 · Cited by 63
Deep learning for wheat ear segmentation and ear density measurement: From heading to maturity
Dandrifosse, S., Ennadifi, E., Carlier, A., Gosselin, B. H., Dumont, B., & Mercatoris, B. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107161 · Cited by 55
Jiang, J., Peng, J., Hu, C., Jian, W., Wang, X., & Liu, W. (2022) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2022.102419 · Cited by 57
Dual Teacher: A Semisupervised Cotraining Framework for Cross-Domain Ship Detection
Zheng, X., Cui, H., Xu, C., & Lu, X. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3287863 · Cited by 53
Zhang, T., Gao, G., & Zhang, X. (2026) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2026.03.001 · Cited by 51
Boosting Ship Detection in SAR Images With Complementary Pretraining Techniques
Bao, W., Huang, M., Zhang, Y.-Q., Xu, Y. H., Liu, X., & Xiang, X. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3109002 · Cited by 50
Wang, S., Anastasiu, D. C., Tang, Z., Chang, M., Yao, Y., Zheng, L., Rahman, M. S., Arya, M. S., Sharma, A., Chakraborty, P., et al. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00722 · Cited by 53
Zhang, H., Hao, C., Song, W., Jiang, B., & Li, B. (2023) · Remote Sensing · doi:10.3390/rs15051249 · Cited by 54
A Real-Time UAV Target Detection Algorithm Based on Edge Computing
Cheng, Q., Wang, H., Zhu, B. B., Shi, Y., & Xie, B. (2023) · Drones · doi:10.3390/drones7020095 · Cited by 45
Ye, X., Pan, J., Shao, F., Liu, G., Lin, J., Xu, D., & Liu, J. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108671 · Cited by 47
Jelli, E., Ohmura, T., Netter, N., Abt, M., Jiménez‐Siebert, E., Neuhaus, K., Rode, D. K. H., Nadell, C. D., & Drescher, K. (2023) · Molecular Microbiology · doi:10.1111/mmi.15064 · Cited by 46
Learning From Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis
Le, K. H., Tran, T. V., Pham, H., Nguyen, H. T., Le, T., & Nguyen, H. Q. (2023) · IEEE Access · doi:10.1109/access.2023.3243845 · Cited by 46
DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds
Ma, T., Yang, X., Zhou, H., Li, X. S., Shi, B., Liu, J., Yang, Y., Liu, Z., He, L., Qiao, Y., et al. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00620 · Cited by 43
Periodically Exchange Teacher-Student for Source-Free Object Detection
Liu, Q., Lin, L., Shen, Z., & Yang, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00590 · Cited by 38
A Deep Learning-Based Generalized System for Detecting Pine Wilt Disease Using RGB-Based UAV Images
You, J., Zhang, R., & Lee, J. (2021) · Remote Sensing · doi:10.3390/rs14010150 · Cited by 40
Litchi Detection in a Complex Natural Environment Using the YOLOv5-Litchi Model
Xie, J., Peng, J., Wang, J., Chen, B., Jing, T., Sun, D., Gao, P., Wang, W., Lu, J., Yetan, R., et al. (2022) · Agronomy · doi:10.3390/agronomy12123054 · Cited by 36
Self-citationIntegrated Learning-Based Pest and Disease Detection Method for Tea Leaves
Wang, Y., Xu, R., Bai, D., & Lin, H. (2023) · Forests · doi:10.3390/f14051012 · Cited by 36
Wheat Head Detection using Deep, Semi-Supervised and Ensemble Learning
Fourati, F., Mseddi, W. S., & Attia, R. (2021) · Canadian Journal of Remote Sensing · doi:10.1080/07038992.2021.1906213 · Cited by 40
Building height calculation for an urban area based on street view images and deep learning
Xu, Z., Zhang, F., Wu, Y., Yang, Y., & Wu, Y. (2022) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12930 · Cited by 35
Low-Light Image Enhancement Framework for Improved Object Detection in Fisheye Lens Datasets
Tran, D. Q., Aboah, A., Jeon, Y., Shoman, M., Park, M., & Park, S. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00699 · Cited by 34
Hybrid CNN-Transformer Model for Accurate Impacted Tooth Detection in Panoramic Radiographs
Küçük, D. B., İmak, A., Özçelik, S. T. A., Çelebi, A., Türkoğlu, M., Şengür, A., & Koundal, D. (2025) · Diagnostics · doi:10.3390/diagnostics15030244 · Cited by 34
Konz, N., Buda, M., Gu, H., Saha, A., Yang, J., Chłędowski, J., Park, J., Witowski, J. S., Geras, K. J., Shoshan, Y., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.0524 · Cited by 33
Toward Better Accuracy-Efficiency Trade-Offs: Divide and Co-Training
Zhao, S., Zhou, L., Wang, W., Cai, D., Lam, T. L., & Xu, Y. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3201602 · Cited by 38
Self-citationMalicious Attacks against Multi-Sensor Fusion in Autonomous Driving
Zhu, Y., Miao, C., Xue, H., Yu, Y., Su, L., & Qiao, C. (2024) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3636534.3649372 · Cited by 36
Automatic caries detection in bitewing radiographs: part I—deep learning
Kunt, L., Kybic, J., Nagyová, V., & Tichý, A. (2023) · Clinical Oral Investigations · doi:10.1007/s00784-023-05335-1 · Cited by 33
MODAN: Multifocal Object Detection Associative Network for Maritime Horizon Surveillance
Yoon, S., Jalal, A., & Cho, J. (2023) · Journal of Marine Science and Engineering · doi:10.3390/jmse11101890 · Cited by 30
EfficientLiteDet: a real-time pedestrian and vehicle detection algorithm
Chintakindi, B. M., Hashmi, M. F., & Keskar, A. G. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01293-y · Cited by 31
Ensemble model for rail surface defects detection
Li, H., Wang, F., Liu, J., Song, H., Hou, Z., & Dai, P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268518 · Cited by 31
A deep learning model for detection of leukocytes under various interference factors
Li, M., Lin, C., Ge, P., Li, L., Song, S., Zhang, H., Lu, L., Liu, X., Zheng, F., Zhang, S., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-29331-3 · Cited by 30
Malawade, A. V., Mortlock, T., & Faruque, M. A. A. (2022) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3489517.3530489 · Cited by 29
Photometric-Stereo-Based Defect Detection System for Metal Parts
Cao, Y., Ding, B., Chen, J., Liu, W., Guo, P., Huang, L., & Yang, J. (2022) · Sensors · doi:10.3390/s22218374 · Cited by 29
Transformer-based mass detection in digital mammograms
Tarifa, A. S. B., Marrocco, C., Molinara, M., Tortorella, F., & Bria, A. (2023) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-023-04517-9 · Cited by 32
Alkhalefah, S., Al-Turaiki, I. M., & Altwaijry, Ν. (2025) · Healthcare · doi:10.3390/healthcare13060648 · Cited by 28
Improving deep learning sorghum head detection through test time augmentation
Gonzalo‐Martín, C., Garcia-Pedrero, A. M., & Lillo‐Saavedra, M. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106179 · Cited by 31
Misaligned RGB-Infrared Object Detection via Adaptive Dual-Discrepancy Calibration
He, M., Wu, Q., Ngan, K. N., Jiang, F., Meng, F., & Xu, L. (2023) · Remote Sensing · doi:10.3390/rs15194887 · Cited by 30
Chun, D., Lee, S., & Kim, H. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2023.3348662 · Cited by 25
GCD-YOLOv5: An Armored Target Recognition Algorithm in Complex Environments Based on Array Lidar
Dai, J., Zhao, X., Li, L., & Xiao, F. (2022) · IEEE photonics journal · doi:10.1109/jphot.2022.3185304 · Cited by 25
Ye, Z., Guo, Q., Wei, J., Zhang, J., Zhang, H., Bian, L., Guo, S., Zheng, X., & Cao, S. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.991929 · Cited by 25
Towards Comprehensive Monocular Depth Estimation: Multiple Heads are Better Than One
Shao, S., Li, R., Pei, Z., Liu, Z., Chen, W., Zhu, W., Wu, X., & Zhang, B. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3224810 · Cited by 25
Mao, K., Jin, R., Ying, L., Yao, X., Dai, G., & Fang, K. (2023) · IEEE Systems Journal · doi:10.1109/jsyst.2023.3292113 · Cited by 25
Robust Automatic Motorcycle Helmet Violation Detection for an Intelligent Transportation System
Tran, D. N., Pham, L. H., Jeon, H., Nguyen, H.-H., Jeon, H.-M., Tran, T. H.-P., & Jeon, J. W. (2023) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00563 · Cited by 23
Li, J., Gao, B., Woo, W. L., Xu, J., Liu, L., & Zeng, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3344145 · Cited by 25
Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge
Sogancioglu, E., van Ginneken, B., Behrendt, F., Bengs, M., Schlaefer, A., Radu, M., Xu, D., Sheng, K., Scalzo, F., Marcus, E., et al. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3382042 · Cited by 25
MV2DFusion: Leveraging Modality-Specific Object Semantics for Multi-Modal 3D Detection
Wang, Z., Huang, Z., Gao, Y., Wang, N., & Liu, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3609348 · Cited by 30
A Forest Fire Identification System Based on Weighted Fusion Algorithm
Qian, J., & Lin, H. (2022) · Forests · doi:10.3390/f13081301 · Cited by 23
Enhancing Tuta absoluta Detection on Tomato Plants: Ensemble Techniques and Deep Learning
Giakoumoglou, N., Pechlivani, E. M., Frangakis, N., & Tzovaras, D. K. (2023) · AI · doi:10.3390/ai4040050 · Cited by 23
Wang, C., Lin, T., Chen, G., Lee, M., Tay, J., Wu, C., Wu, M., Roth, H. R., Yang, D., Zhao, C., et al. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-02023-1 · Cited by 28
Self-citationYang, X., Yu, P., Zhang, H., Zhang, R., Liu, Y., Li, H., Sun, P., Liu, X., Wu, Y., Jia, X., et al. (2023) · Stroke · doi:10.1161/strokeaha.122.041520 · Cited by 22
Bushra, F., Chowdhury, M. E. H., Sarmun, R., Kabir, S., Said, M., Zoghoul, S. B. M., Mushtak, A., Al‐Hashimi, I., Alqahtani, A., & Hasan, A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123029 · Cited by 23
Özbılge, E., Güler, E., & Ozbılge, E. (2024) · IEEE Access · doi:10.1109/access.2024.3393410 · Cited by 26
Robust Data Augmentation and Ensemble Method for Object Detection in Fisheye Camera Images
Duong, V. H., Nguyen, D. Q., Luong, T. V., Vu, H., & Nguyen, T. C. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00695 · Cited by 22
MVA2023 Small Object Detection Challenge for Spotting Birds: Dataset, Methods, and Results
Kondo, Y., Ukita, N., Yamaguchi, T., Hou, H.-Y., Shen, M.-Y., Hsu, C.-C., Huang, E.-M., Huang, Y., Xia, Y.-C., Wang, C.-Y., et al. (2023) · 2023 18th International Conference on Machine Vision and Applications (MVA) · doi:10.23919/mva57639.2023.10215935 · Cited by 22
Automatic Characterization of Boulders on Planetary Surfaces From High‐Resolution Satellite Images
Prieur, N. C., Amaro, B., Gonzalez, E., Kerner, H., Medvedev, S., Rubanenko, L., Werner, S. C., Xiao, Z., Zastrozhnov, D., & Lapôtre, M. G. A. (2023) · Journal of Geophysical Research Planets · doi:10.1029/2023je008013 · Cited by 23
Gao, H., Sun, Y., Xiao, J., Fang, D., Xu, Y., & Wei, W. (2024) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2024.3427060 · Cited by 21
Wei, C., Bai, Y., Liu, C., Zhu, Y., Wang, C., & Li, X. (2024) · Scientific Reports · doi:10.1038/s41598-024-63501-1 · Cited by 21
CrossRectify: Leveraging disagreement for semi-supervised object detection
Ma, C., Pan, X., Ye, Q., Tang, F., Dong, W., & Xu, C. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.109280 · Cited by 20
Optimizing YOLOv7 for semiconductor defect detection
Dehaerne, E., Dey, B., Halder, S., & Gendt, S. D. (2023) · Metrology, Inspection, and Process Control XXXVI · doi:10.1117/12.2657564 · Cited by 20
Pulmonary nodule detection based on model fusion and adaptive false positive reduction
Xiong, Y., Deng, L., & Wang, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121890 · Cited by 19
Lymph node detection in T2 MRI with transformers
Mathai, T. S., Lee, S., Elton, D. C., Shen, T. C., Peng, Y., Lu, Z., & Summers, R. M. (2022) · SPIE Medical Imaging: Computer-Aided Diagnosis · doi:10.1117/12.2613273 · Cited by 19
Confidence Propagation Cluster: Unleash Full Potential of Object Detectors
Shen, Y., Jiang, W., Xu, Z. X., Li, R., & Kwon, J. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00122 · Cited by 20
Hu, S., Liu, C.-H., Dutta, J., Chang, M., Lyu, S., & Ramakrishnan, N. (2022) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00485 · Cited by 19
TUN-Det: A Novel Network for Thyroid Ultrasound Nodule Detection
Shahroudnejad, A., Qin, X., Balachandran, S., Dehghan, M., Zonoobi, D., Jaremko, J. L., Kapur, J., Jägersand, M., Noga, M., & Punithakumar, K. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_62 · Cited by 20
Detecting COVID-19 Opacity in X-ray Images Using YOLO and RetinaNet Ensemble
Tiwari, V., Singhal, A., & Dhankhar, N. (2022) · IEEE Delhi Section Conference (DELCON) · doi:10.1109/delcon54057.2022.9753272 · Cited by 19
A systematic approach to deep learning-based nodule detection in chest radiographs
Behrendt, F., Bengs, M., Bhattacharya, D., Krüger, J., Opfer, R., & Schlaefer, A. (2023) · Scientific Reports · doi:10.1038/s41598-023-37270-2 · Cited by 20
Ensemble Fusion for Small Object Detection
Hou, H.-Y., Shen, M.-Y., Hsu, C.-C., Huang, E.-M., Huang, Y., Xia, Y.-C., Wang, C.-Y., & Lee, C. (2023) · doi:10.23919/mva57639.2023.10215748 · Cited by 19
Cross-domain Federated Object Detection
Su, S., Li, B., Zhang, C., Yang, M., & Xue, X. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00254 · Cited by 20
A Knowledge-Guided Fusion Visualisation Method of Digital Twin Scenes for Mountain Highways
Tang, R., Zhu, J., Ying, R., Ding, Y., Wu, J., Guo, Y., & Xie, Y. (2023) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi12100424 · Cited by 18
A Bag of Tricks for Fine-Grained Roof Extraction
Hu, J., Huang, Z., Shen, F., He, D., & Xian, Q. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10283210 · Cited by 18
CooperFuse: A Real-Time Cooperative Perception Fusion Framework
Zheng, Z., Xia, X., Gao, L., Xiang, H., & Ma, J. (2024) · IEEE Intelligent Vehicles Symposium · doi:10.1109/iv55156.2024.10588758 · Cited by 18
1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain AdaptationPreprint
Ding, Z., Hu, Y., Ge, R., Huang, L., Chen, S., Wang, Y., & Liao, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.15505 · Cited by 18
COTS recognition and detection based on Improved YOLO v5 model
Jiang, Y. (2022) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp54964.2022.9778430 · Cited by 17
Bai, Z., Bi, D., Wu, J., Wang, M., Zheng, Q., & Chen, L. (2022) · Actuators · doi:10.3390/act11100272 · Cited by 18
Khan, A. A. R., Akram, M. U., & Nazir, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0280352 · Cited by 22
Metadata
- Authors
- Solovyev, R., Wang, W., & Gabruseva, T.
- Affiliations
- Russian Academy of Sciences (RU)
- National University of Singapore (SG)
- Cork University Hospital (IE)
- Corresponding author
- Solovyev, Roman
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2021-03
- Volume
- 107
- Pages
- 104117
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W3127743092
- Semantic Scholar
- 2765484bdd3e5d8674917efc98145daa1a71e9fa
- Cited by
- 543
- References
- 36
- Citations by year
- 2026: 108 · 2025: 98 · 2024: 92 · 2023: 130 · 2022: 76 · 2021: 36
- FWCI
- 30.03
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Image and Vision Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- object detection, model ensembling, confidence scores, 3D object detection, autonomous driving, Waymo Open Dataset
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-07, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-07 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
