Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism
文献类型：期刊论文
| 作者 | Liu, Can1,2; Wang, Kaige3; Li, Qing1; Zhao, Fazhan1; Zhao, Kun4 ; Ma, Hongtu4
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| 刊名 | NEURAL NETWORKS
 |
| 出版日期 | 2024-02-01 |
| 卷号 | 170页码:276-284 |
| 关键词 | Object detection Bounding box regression Loss function design Focusing mechanism |
| ISSN号 | 0893-6080 |
| DOI | 10.1016/j.neunet.2023.11.041 |
| 通讯作者 | Liu, Can(canliu@whu.edu.cn) |
| 英文摘要 | Bounding box regression (BBR) is one of the core tasks in object detection, and the BBR loss function significantly impacts its performance. However, we have observed that existing IoU-based loss functions suffer from unreasonable penalty factors, leading to anchor boxes expanding during regression and significantly slowing down convergence. To address this issue, we intensively analyzed the reasons for anchor box enlargement. In response, we propose a Powerful-IoU (PIoU) loss function, which combines a target size-adaptive penalty factor and a gradient-adjusting function based on anchor box quality. The PIoU loss guides anchor boxes to regress along efficient paths, resulting in faster convergence than existing IoU-based losses. Additionally, we investigate the focusing mechanism and introduce a non-monotonic attention layer that was combined with PIoU to obtain a new loss function PIoU v2. PIoU v2 loss enhances the capability to focus on anchor boxes of medium quality. By incorporating PIoU v2 into popular object detectors such as YOLOv8 and DINO, we achieved an increase in average precision (AP) and improved performance compared to their original loss functions on the MS COCO and PASCAL VOC datasets, thus validating the effectiveness of our proposed improvement strategies. |
| WOS关键词 | OBJECT DETECTION |
| 资助项目 | National Key Research and Development Program of China[2021YFB3100904] |
| WOS研究方向 | Computer Science ; Neurosciences & Neurology |
| 语种 | 英语 |
| WOS记录号 | WOS:001125595300001 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 资助机构 | National Key Research and Development Program of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54937]  |
| 专题 | 脑图谱与类脑智能实验室 |
| 通讯作者 | Liu, Can |
| 作者单位 | 1.Chinese Acad Sci, Inst Microelect, Beijing 100029, Peoples R China 2.Univ Chinese Acad Sci, Sch Integrated Circuits, Beijing 100020, Peoples R China 3.China Acad Aerosp Sci & Innovat, Beijing 100048, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Can,Wang, Kaige,Li, Qing,et al. Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism[J]. NEURAL NETWORKS,2024,170:276-284. |
| APA | Liu, Can,Wang, Kaige,Li, Qing,Zhao, Fazhan,Zhao, Kun,&Ma, Hongtu.(2024).Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism.NEURAL NETWORKS,170,276-284. |
| MLA | Liu, Can,et al."Powerful-IoU: More straightforward and faster bounding box regression loss with a nonmonotonic focusing mechanism".NEURAL NETWORKS 170(2024):276-284. |
入库方式： OAI收割
来源：自动化研究所
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