The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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
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
(2016) · Cited by 31,590
Feature Pyramid Networks for Object Detection
Lin, T.-Y., Dollar, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.106 · Cited by 30,939
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
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
EfficientDet: Scalable and Efficient Object Detection
Tan, M., Pang, R., & Le, Q. V. (2019) · arXiv · Cited by 9,841 · cited 2×
“In order to extract more powerful features from input image, we employ CBNet [24] as our backbone network. we use BiFPN [35] as a feature fusion neck to enhance the information flow across the highly semantic and spatially finer features.”
Method
Cited by
Detection and tracking meet drones challenge
Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336
Self-citationQueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection
Yang, C., Huang, Z., & Wang, N. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01330 · Cited by 634
RFLA: Gaussian Receptive Field Based Label Assignment for Tiny Object Detection
Xu, C., Wang, J., Yang, W., Yu, H., Yu, L., & Xia, G.-S. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-20077-9_31 · Cited by 308
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV Applications
Zhang, P., Zhong, Y., & Li, X. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00011 · Cited by 266
Dense and Small Object Detection in UAV Vision Based on Cascade Network
Zhang, X., Izquierdo, E., & Chandramouli, K. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00020 · Cited by 129
Metadata
- Authors
- Du, D., Wen, L., Zhu, P., Fan, H., Hu, Q., Ling, H., Shah, M., Pan, J., Axenopoulos, A., Schumann, A., Psaltis, A., Jain, A., Dong, B., Li, C., Chen, C., Duan, C., Zhang, C., Stadler, D., Pailla, D. R., Yin, D., Khan, F., Meng, F., Gao, G., Zhang, G., Chen, H., Zhou, H., Xie, H., Qiu, H., Li, H., Athanasiadis, I., Cui, J., Zhou, J., Ko, J., Lee, J., Yu, J., Yoo, J., Sommer, L. W., Xiong, L., Schleiss, M., Yang, M.-H., Liu, M., Zhang, M., Mandal, M., Daras, P., Narang, P., Liu, Q., Shi, Q., Lin, Q., Ramaprasad, R., Wang, S., Mehta, S., Li, S., Huang, S., Moon, S., Zhao, T., Sun, T., Guo, W., Tian, W., Qin, W., Yu, W., Lin, W., Zhao, X., Jia, X., He, X., Zhao, X., Liu, X., Ding, Y., Luo, Y., Xiao, Y., Wang, Y., Liu, Y., Kim, Y., Sun, Y., Yao, Y., Huang, Y., Gong, Z., Xu, Z., Luo, Z., Cao, Z., Wei, Z., Fan, Z., Song, Z., & Liu, Z.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 692-712
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3119129845
- Cited by
- 259
- References
- 52
- FWCI
- 5.76
- Type
- conference paper
- Language
- en
- Open access
- closed
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.
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results. https://app.cochat.ai/papers/10.1007/978-3-030-66823-5_42
- The paper
- Du, D., Wen, L., Zhu, P., Fan, H., Hu, Q., Ling, H., Shah, M., Pan, J., Axenopoulos, A., Schumann, A., Psaltis, A., Jain, A., Dong, B., Li, C., Chen, C., Duan, C., Zhang, C., Stadler, D., Pailla, D. R., . . . Liu, Z. (2020). VisDrone-DET2020: The Vision Meets Drone Object Detection in Image Challenge Results. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-66823-5_42
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.