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Cited by 3,001Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningVisual Attention and Saliency Detection

YOLOv10: Real-Time End-to-End Object Detection

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Wang, Ao, Chen, Hui, Liu, Lihao, Chen, Kai, Lin, Zijia, Han, Jungong, Ding, Guiguang·2024·Advances in Neural Information Processing Systems 37
DOI 10.52202/079017-3429
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Abstract

Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8$\times$ faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8$\times$ smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance.

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  • YOLOv10 outperformed previous versions in tasks such as facemask detection and autonomous vehicle applications.

    “Additionally, the model's improved attention mechanisms enabled better detection of small and occluded objects, allowing YOLOv10 to outperform previous versions in tasks such as facemask detection and autonomous vehicle applications.”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [138]

    Section 4.2 reports that YOLOv10 outperformed earlier YOLO versions on the evaluated object-detection benchmark. Section 4.1 identifies that benchmark as COCO; the paper does not report comparative results for facemask detection or autonomous-vehicle applications. Section 3.2 describes a partial self-attention module but does not establish that it improved detection of small or occluded objects. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • YOLOv10 introduced the C3k2 block, improving feature aggregation while reducing computational overhead.

    “A key advancement in YOLOv10 was the introduction of the C3k2 block, an innovative feature that greatly improved feature aggregation while reducing computational overhead [138].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [138]

    Section 3.2 describes introducing a compact inverted block (CIB) and using rank-guided placement to improve efficiency without compromising performance. The cited paper does not identify that block as C3k2 or attribute improved feature aggregation to it, so those parts of the claim are unsupported. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • C3k2 allowed YOLOv10 to maintain high accuracy in resource-constrained environments.

    “This allowed YOLOv10 to maintain high accuracy even in resource-constrained environments, making it ideal for deployment on edge devices.”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [138]

    Section 3.2 says YOLOv10’s compact block design can improve efficiency without compromising performance, supporting the general accuracy–efficiency point. Our copy does not identify that block as C3k2 or establish that C3k2 enables accuracy in resource-constrained environments or makes the model ideal for edge devices. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • YOLOv10's improved attention mechanisms enabled better detection of small and occluded objects.

    “Additionally, the model's improved attention mechanisms enabled better detection of small and occluded objects, allowing YOLOv10 to outperform previous versions in tasks such as facemask detection and autonomous vehicle applications.”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [138]

    The Introduction (section 1) describes a partial self-attention module intended to improve YOLOv10’s performance, and the ablation analysis reports a 0.3% AP improvement. The paper does not attribute better detection of small or occluded objects to that module or report facemask-detection or autonomous-vehicle task results. It mentions autonomous driving as a potential application, not a tested setting in which YOLOv10 outperformed earlier versions. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 77

  1. Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016. · arXiv
  2. Daniel Bogdoll, Maximilian Nitsche, and J Marius Zöllner. Anomaly detection in autonomous driving: A survey. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4488-4499, 2022.
  3. Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020.
  4. Qiang Chen, Xiaokang Chen, Jian Wang, Shan Zhang, Kun Yao, Haocheng Feng, Junyu Han, Errui Ding, Gang Zeng, and Jingdong Wang. Group detr: Fast detr training with group-wise one- to-many assignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6633-6642, 2023.
  5. Yiqun Chen, Qiang Chen, Qinghao Hu, and Jian Cheng. Date: Dual assignment for end-to-end fully convolutional object detection. arXiv preprint arXiv:2211.13859, 2022. · arXiv
  6. Yiqun Chen, Qiang Chen, Peize Sun, Shoufa Chen, Jingdong Wang, and Jian Cheng. Enhancing your trained detrs with box refinement. arXiv preprint arXiv:2307.11828, 2023. · arXiv
  7. Yuming Chen, Xinbin Yuan, Ruiqi Wu, Jiabao Wang, Qibin Hou, and Ming-Ming Cheng. Yolo-ms: rethinking multi-scale representation learning for real-time object detection. arXiv preprint arXiv:2308.05480, 2023. · arXiv
  8. François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017.
  9. Xiaohan Ding, Xiangyu Zhang, Jungong Han, and Guiguang Ding. Scaling up your kernels to 31x31: Revisiting large kernel design in cnns. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11963-11975, 2022.
  10. Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, and Jian Sun. Repvgg: Making vgg-style convnets great again. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13733-13742, 2021.
  11. Douglas Henke Dos Reis, Daniel Welfer, Marco Antonio De Souza Leite Cuadros, and Daniel Fernando Tello Gamarra. Mobile robot navigation using an object recognition software with rgbd images and the yolo algorithm. Applied Artificial Intelligence, 33(14):1290-1305, 2019.
  12. Kaiwen Duan, Song Bai, Lingxi Xie, Honggang Qi, Qingming Huang, and Qi Tian. Centernet: Keypoint triplets for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6569-6578, 2019.
  13. Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12873-12883, 2021.
  14. Chengjian Feng, Yujie Zhong, Yu Gao, Matthew R Scott, and Weilin Huang. Tood: Task-aligned one-stage object detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 3490-3499. IEEE Computer Society, 2021.
  15. Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael Jordan, and Zheng-Jun Zha. Rank diminishing in deep neural networks. Advances in Neural Information Processing Systems, 35:33054-33065, 2022.
  16. Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430, 2021. · arXiv
  17. Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph. Simple copy-paste is a strong data augmentation method for instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2918-2928, 2021.
  18. Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440-1448, 2015.
  19. Benjamin Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, and Matthijs Douze. Levit: a vision transformer in convnet's clothing for faster inference. In Proceedings of the IEEE/CVF international conference on computer vision, pages 12259- 12269, 2021.
  20. Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017.
  21. Jan Hosang, Rodrigo Benenson, and Bernt Schiele. Learning non-maximum suppression. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4507-4515, 2017.
  22. Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017. · arXiv
  23. Han Hu, Jiayuan Gu, Zheng Zhang, Jifeng Dai, and Yichen Wei. Relation networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3588-3597, 2018.
  24. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448-456. pmlr, 2015.
  25. Ding Jia, Yuhui Yuan, Haodi He, Xiaopei Wu, Haojun Yu, Weihong Lin, Lei Sun, Chao Zhang, and Han Hu. Detrs with hybrid matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 19702-19712, 2023.
  26. Chuyi Li, Lulu Li, Yifei Geng, Hongliang Jiang, Meng Cheng, Bo Zhang, Zaidan Ke, Xiaoming Xu, and Xiangxiang Chu. Yolov6 v3.0: A full-scale reloading. arXiv preprint arXiv:2301.05586, 2023. · arXiv
  27. Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. Dn-detr: Accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 13619-13627, 2022.
  28. Xiang Li, Wenhai Wang, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang. Generalized focal loss v2: Learning reliable localization quality estimation for dense object detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11632-11641, 2021.
  29. Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems, 33:21002-21012, 2020.
  30. Ming Lin, Hesen Chen, Xiuyu Sun, Qi Qian, Hao Li, and Rong Jin. Neural architecture design for gpu-efficient networks. arXiv preprint arXiv:2006.14090, 2020. · arXiv
  31. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980-2988, 2017.
  32. Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision-ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pages 740-755. Springer, 2014.
  33. Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, and Lei Zhang. Dab-detr: Dynamic anchor boxes are better queries for detr. arXiv preprint arXiv:2201.12329, 2022. · arXiv
  34. Shu Liu, Lu Qi, Haifang Qin, Jianping Shi, and Jiaya Jia. Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8759-8768, 2018.
  35. Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10012-10022, 2021.
  36. Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976-11986, 2022.
  37. Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel. Understanding the effective receptive field in deep convolutional neural networks. Advances in neural information processing systems, 29, 2016.
  38. Chengqi Lyu, Wenwei Zhang, Haian Huang, Yue Zhou, Yudong Wang, Yanyi Liu, Shilong Zhang, and Kai Chen. Rtmdet: An empirical study of designing real-time object detectors. arXiv preprint arXiv:2212.07784, 2022. · arXiv
  39. Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, and Jingdong Wang. Conditional detr for fast training convergence. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3651-3660, 2021.
  40. Haodong Ouyang. Deyov2: Rank feature with greedy matching for end-to-end object detection. arXiv preprint arXiv:2306.09165, 2023. · arXiv
  41. Haodong Ouyang. Deyov3: Detr with yolo for real-time object detection. arXiv preprint arXiv:2309.11851, 2023. · arXiv
  42. Haodong Ouyang. Deyo: Detr with yolo for end-to-end object detection. arXiv preprint arXiv:2402.16370, 2024. · arXiv
  43. Victor M Panaretos and Yoav Zemel. Statistical aspects of wasserstein distances. Annual review of statistics and its application, 6:405-431, 2019.
  44. Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
  45. Joseph Redmon and Ali Farhadi. Yolo9000: Better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
  46. Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4510-4520, 2018.
  47. Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8430-8439, 2019.
  48. Russell Stewart, Mykhaylo Andriluka, and Andrew Y Ng. End-to-end people detection in crowded scenes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2325-2333, 2016.
  49. Yuchen Su, Zhineng Chen, Zhiwen Shao, Yuning Du, Zhilong Ji, Jinfeng Bai, Yong Zhou, and Yu-Gang Jiang. Lranet: Towards accurate and efficient scene text detection with low-rank approximation network. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 4979-4987, 2024.
  50. Peize Sun, Yi Jiang, Enze Xie, Wenqi Shao, Zehuan Yuan, Changhu Wang, and Ping Luo. What makes for end-to-end object detection? In International Conference on Machine Learning, pages 9934-9944. PMLR, 2021.
  51. Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, et al. Sparse r-cnn: End-to-end object detection with learnable proposals. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 14454-14463, 2021.
  52. Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Fcos: A simple and strong anchor-free object detector. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4):1922-1933, 2020.
  53. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.
  54. Ao Wang, Hui Chen, Zijia Lin, Hengjun Pu, and Guiguang Ding. Repvit: Revisiting mobile cnn from vit perspective. arXiv preprint arXiv:2307.09283, 2023. · arXiv
  55. Chengcheng Wang, Wei He, Ying Nie, Jianyuan Guo, Chuanjian Liu, Yunhe Wang, and Kai Han. Gold-yolo: Efficient object detector via gather-and-distribute mechanism. Advances in Neural Information Processing Systems, 36, 2024.
  56. Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. Scaled-yolov4: Scaling cross stage partial network. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 13029-13038, 2021.
  57. Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. Yolov7: Trainable bag-of- freebies sets new state-of-the-art for real-time object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7464-7475, 2023.
  58. Chien-Yao Wang, Hong-Yuan Mark Liao, Yueh-Hua Wu, Ping-Yang Chen, Jun-Wei Hsieh, and I-Hau Yeh. Cspnet: A new backbone that can enhance learning capability of cnn. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 390-391, 2020.
  59. Chien-Yao Wang, Hong-Yuan Mark Liao, and I-Hau Yeh. Designing network design strategies through gradient path analysis. arXiv preprint arXiv:2211.04800, 2022. · arXiv
  60. Chien-Yao Wang, I-Hau Yeh, and Hong-Yuan Mark Liao. Yolov9: Learning what you want to learn using programmable gradient information. arXiv preprint arXiv:2402.13616, 2024. · arXiv
  61. Jianfeng Wang, Lin Song, Zeming Li, Hongbin Sun, Jian Sun, and Nanning Zheng. End-to-end object detection with fully convolutional network. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 15849-15858, 2021.
  62. Yingming Wang, Xiangyu Zhang, Tong Yang, and Jian Sun. Anchor detr: Query design for transformer-based detector. In Proceedings of the AAAI conference on artificial intelligence, volume 36, pages 2567-2575, 2022.
  63. Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, and Lei Zhang. Cvt: Introducing convolutions to vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 22-31, 2021.
  64. Haiyang Xu, Zhichao Zhou, Dongliang He, Fu Li, and Jingdong Wang. Vision transformer with attention map hallucination and ffn compaction. arXiv preprint arXiv:2306.10875, 2023. · arXiv
  65. Shangliang Xu, Xinxin Wang, Wenyu Lv, Qinyao Chang, Cheng Cui, Kaipeng Deng, Guanzhong Wang, Qingqing Dang, Shengyu Wei, Yuning Du, et al. Pp-yoloe: An evolved version of yolo. arXiv preprint arXiv:2203.16250, 2022. · arXiv
  66. Xianzhe Xu, Yiqi Jiang, Weihua Chen, Yilun Huang, Yuan Zhang, and Xiuyu Sun. Damo-yolo: A report on real-time object detection design. arXiv preprint arXiv:2211.15444, 2022. · arXiv
  67. Fangao Zeng, Bin Dong, Yuang Zhang, Tiancai Wang, Xiangyu Zhang, and Yichen Wei. Motr: End-to-end multiple-object tracking with transformer. In European Conference on Computer Vision, pages 659-675. Springer, 2022.
  68. Hao Zhang, Feng Li, Shilong Liu, Lei Zhang, Hang Su, Jun Zhu, Lionel M Ni, and Heung- Yeung Shum. Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv preprint arXiv:2203.03605, 2022. · arXiv
  69. Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. · arXiv
  70. Shifeng Zhang, Cheng Chi, Yongqiang Yao, Zhen Lei, and Stan Z Li. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9759-9768, 2020.
  71. Wenqiang Zhang, Zilong Huang, Guozhong Luo, Tao Chen, Xinggang Wang, Wenyu Liu, Gang Yu, and Chunhua Shen. Topformer: Token pyramid transformer for mobile semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12083-12093, 2022.
  72. Chuyang Zhao, Yifan Sun, Wenhao Wang, Qiang Chen, Errui Ding, Yi Yang, and Jingdong Wang. Ms-detr: Efficient detr training with mixed supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17027-17036, 2024.
  73. Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, and Jie Chen. Detrs beat yolos on real-time object detection. arXiv preprint arXiv:2304.08069, 2023. · arXiv
  74. Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren. Distance-iou loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence, volume 34, pages 12993-13000, 2020.
  75. Qiang Zhou and Chaohui Yu. Object detection made simpler by eliminating heuristic nms. IEEE Transactions on Multimedia, 2023.
  76. Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159, 2020. · arXiv
  77. Zhuofan Zong, Guanglu Song, and Yu Liu. Detrs with collaborative hybrid assignments training. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6748-6758, 2023.

Cited by

Sorted by citations

Authors
Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., & Ding, G.
ORCID
Venue
Advances in Neural Information Processing Systems 37
Publisher
Neural Information Processing Systems Foundation, Inc. (NeurIPS)
Published
2024
Pages
107984-108011
ISSN
1049-5258 (ISSN-L)
DOI
10.52202/079017-3429
OpenAlex
W4415795831
Cited by
3,001
References
0
Citations by year
2026: 1830 · 2025: 1001 · 2024: 170
FWCI
619.47
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • neural information processing systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
National Natural Science Foundation of China (61925107)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
Keywords
YOLOv10, object detection, real-time detection, end-to-end detection, YOLO

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