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Zhaohui Zheng, Ping Wang, Dongwei Ren, Wei Liu, Rongguang Ye, Qinghua Hu, Wangmeng Zuo
Deep learning-based object detection and instance segmentation have achieved unprecedented progress. In this article, we propose complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding-box regression and nonmaximum suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency. In particular, we consider three geometric factors, that is: 1) overlap area; 2) normalized central-point distance; and 3) aspect ratio, which are crucial for measuring bounding-box regression in object detection and instance segmentation. The three geometric factors are then incorporated into CIoU loss for better distinguishing difficult regression cases. The training of deep models using CIoU loss results in consistent AP and AR improvements in comparison to widely adopted$\ell _{n}$-norm loss and IoU-based loss. Furthermore, we propose Cluster-NMS, where NMS during inference is done by implicitly clustering detected boxes and usually requires fewer iterations. Cluster-NMS is very efficient due to its pure GPU implementation, and geometric factors can be incorporated to improve both AP and AR. In the experiments, CIoU loss and Cluster-NMS have been applied to state-of-the-art instance segmentation (e.g., YOLACT and BlendMask-RT), and object detection (e.g., YOLO v3, SSD, and Faster R-CNN) models. Taking YOLACT on MS COCO as an example, our method achieves performance gains as +1.7 AP and +6.2 AR100for object detection, and +1.1 AP and +3.5 AR100for instance segmentation, with 27.1 FPS on one NVIDIA GTX 1080Ti GPU. All the source code and trained models are available athttps://github.com/Zzh-tju/CIoU.
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Zheng, Z., Wang, P., Ren, D., Liu, W., Ye, R., Hu, Q., & Zuo, W. (2021). Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation. IEEE Transactions on Cybernetics, 52(8), 8574–8586. https://doi.org/10.1109/tcyb.2021.3095305
Zheng, Zhaohui, et al. "Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation." IEEE Transactions on Cybernetics, vol. 52, no. 8, 2021, pp. 8574–8586, https://doi.org/10.1109/tcyb.2021.3095305.
Zheng, Zhaohui, Ping Wang, Dongwei Ren, Wei Liu, Rongguang Ye, Qinghua Hu, and Wangmeng Zuo. "Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation." IEEE Transactions on Cybernetics 52, no. 8 (2021): 8574–8586. https://doi.org/10.1109/tcyb.2021.3095305.
Zheng Z., Wang P., Ren D. [и др.] Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation // IEEE Transactions on Cybernetics. — 2021. — Vol. 52, № 8. — P. 8574–8586. — URL: https://doi.org/10.1109/tcyb.2021.3095305
@article{zheng2021enhancing,
title = {{Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation}},
author = {Zhaohui Zheng and Ping Wang and Dongwei Ren and Wei Liu and Rongguang Ye and Qinghua Hu and Wangmeng Zuo},
journal = {IEEE Transactions on Cybernetics},
year = {2021},
volume = {52},
number = {8},
pages = {8574--8586},
doi = {10.1109/tcyb.2021.3095305},
url = {https://doi.org/10.1109/tcyb.2021.3095305}
}TY - JOUR AU - Zheng, Zhaohui AU - Wang, Ping AU - Ren, Dongwei AU - Liu, Wei AU - Ye, Rongguang AU - Hu, Qinghua AU - Zuo, Wangmeng TI - Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation JO - IEEE Transactions on Cybernetics VL - 52 IS - 8 SP - 8574 EP - 8586 PY - 2021 DA - 2021/08/26 DO - 10.1109/tcyb.2021.3095305 UR - https://doi.org/10.1109/tcyb.2021.3095305 ER -
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