Knowledge Commons of Institute of Automation,CAS
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling | |
Yang, Li1,2; Xu, Yan3; Wang, Shaoru1,2; Yuan, Chunfeng1; Zhang, Ziqi1,2; Li, Bing1,4; Hu, Weiming1,2,5 | |
发表期刊 | IEEE Transactions on Image Processing |
ISSN | 1057-7149 |
2022 | |
卷号 | 31页码:5121-5133 |
摘要 | Recent one-stage object detectors follow a per-pixel prediction approach that predicts both the object category scores and boundary positions from every single grid location. However, the most suitable positions for inferring different targets, i.e., the object category and boundaries, are generally different. Predicting all these targets from the same grid location thus may lead to sub-optimal results. In this paper, we analyze the suitable inference positions for object category and boundaries, and propose a prediction-target-decoupled detector named PDNet to establish a more flexible detection paradigm. Our PDNet with the prediction decoupling mechanism encodes different targets separately in different locations. A learnable prediction collection module is devised with two sets of dynamic points, i.e., dynamic boundary points and semantic points, to collect and aggregate the predictions from the favorable regions for localization and classification. We adopt a two-step strategy to learn these dynamic point positions, where the prior positions are estimated for different targets first, and the network further predicts residual offsets to the positions with better perceptions of the object properties. Extensive experiments on the MS COCO benchmark demonstrate the effectiveness and efficiency of our method. With a single ResNeXt-64x4d-101-DCN as the backbone, our detector achieves 50.1 AP with single-scale testing, which outperforms the state-of-the-art methods by an appreciable margin under the same experimental settings. Moreover, our detector is highly efficient as a one-stage framework. Our code is public at https://github.com/yangli18/PDNet. |
关键词 | Object detection prediction decoupling convolutional neural network |
DOI | 10.1109/TIP.2022.3193223 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2020AAA0106800] ; Beijing Natural Science Foundation[JQ21017] ; Beijing Natural Science Foundation[4224091] ; National Natural Science Foundation of China[61972397] ; National Natural Science Foundation of China[62036011] ; National Natural Science Foundation of China[62192782] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61906192] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC040] ; China Postdoctoral Science Foundation[2021M693402] |
项目资助者 | National Key Research and Development Program of China ; Beijing Natural Science Foundation ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; China Postdoctoral Science Foundation |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000835774000011 |
出版者 | IEEE-Institute of Electrical and Electronics Engineers |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49822 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | Yuan, Chunfeng |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Department of Electronic Engineering, The Chinese University of Hong Kong 4.PeopleAI Inc. 5.CAS Center for Excellence in Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Yang, Li,Xu, Yan,Wang, Shaoru,et al. PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling[J]. IEEE Transactions on Image Processing,2022,31:5121-5133. |
APA | Yang, Li.,Xu, Yan.,Wang, Shaoru.,Yuan, Chunfeng.,Zhang, Ziqi.,...&Hu, Weiming.(2022).PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling.IEEE Transactions on Image Processing,31,5121-5133. |
MLA | Yang, Li,et al."PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling".IEEE Transactions on Image Processing 31(2022):5121-5133. |
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PDNet_Toward_Better_(3190KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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