A comprehensive review of object detection with deep learning
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YOLO predicts bounding boxes and class probabilities simultaneously in a single forward pass, which sets it apart from traditional multi-stage detection methods.
“YOLO's approach simultaneously predicting bounding boxes and class probabilities in a single forward pass sets it apart from traditional multi-stage detection methods [5-7].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [6]
Cites
ImageNet classification with deep convolutional neural networks
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
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Distinctive Image Features from Scale-Invariant Keypoints
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ImageNet Large Scale Visual Recognition Challenge
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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A decision-theoretic generalization of on-line learning and an application to boosting
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Backpropagation Applied to Handwritten Zip Code Recognition
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DOTA: A Large-Scale Dataset for Object Detection in Aerial Images
Xia, G.-S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., & Zhang, L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00418 · Cited by 3,414 · see citing works
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Perceptual Generative Adversarial Networks for Small Object Detection
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Is Faster R-CNN Doing Well for Pedestrian Detection?
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Flow-Guided Feature Aggregation for Video Object Detection
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Weakly Supervised Deep Detection Networks
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Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
- Authors
- Kaur, R., & Singh, S.
- Affiliations
- Panjab University (IN)
- Corresponding author
- Kaur, Ravpreet
- Venue
- Digital Signal Processing
- Publisher
- Elsevier BV
- Published
- 2023-01
- Volume
- 132
- Pages
- 103812
- ISSN
- 1051-2004 (ISSN-L)
- OpenAlex
- W4308516693
- Cited by
- 539
- References
- 15
- Citations by year
- 2026: 113 · 2025: 238 · 2024: 163 · 2023: 25
- FWCI
- 33.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Digital Signal Processing (publishedVersion)
- 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, deep convolutional neural networks, evaluation metrics, Pascal VOC, MS COCO, global features, security applications
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.
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.
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