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Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection

文献类型：期刊论文

作者Lu, Yan-Feng2,3,4; Yu, Qian2,3,4; Gao, Jing-Wen2,3,4; Li, Yi5; Zou, Jun-Cheng1; Qiao, Hong2,3,4
刊名NEUROCOMPUTING
出版日期2022-11-07
卷号513页码:70-82
ISSN号0925-2312
关键词Robust object detection Structural deformation Image detection Spatial transformation
DOI10.1016/j.neucom.2022.09.117
通讯作者Lu, Yan-Feng(yanfeng.lv@ia.ac.cn)
英文摘要Structural information is an essential component for efficient object detection. In many visual detection tasks, the objects with large structural deformation usually make up a large proportion. The shape, con-tour, and internal structure of the objects tend toward dramatic change, which easily causes troubles for efficient object detection. Therefore, how to detect these objects robustly and accurately is one of the sig-nificant challenges. To address this issue, we introduce a Cross Stage Partial connections-based weighted Bi-directional Feature Pyramid Network (CSP-BiFPN), which allows easy and efficient multi-scale feature fusion by cross-stage partial connections. Second, to enhance the model's spatial transformation capacity, the multi-scale feature maps extracted from the YOLO backbone network are processed by an enhanced spatial transformation network (ESTN) for spatial deformations. Based on these architectural modifica-tions and optimizations, we further develop a novel real-time robust object detection model called Bi-STN-YOLO. We evaluate the performance of the proposed method on four image datasets. The experi-mental results demonstrate that the proposed approach achieves significant improvements compared with the typical YOLO families and competitive performance compared to the state-of-the-arts in detec-tion tasks. (c) 2022 Elsevier B.V. All rights reserved.
WOS关键词ALIGNMENT
资助项目Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; National Natural Science Foundation of China ; [L211023] ; [2020AAA0105900] ; [91948303]
WOS研究方向Computer Science
语种英语
出版者ELSEVIER
WOS记录号WOS:000866415400007
资助机构Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/50278]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
通讯作者Lu, Yan-Feng
作者单位1.Huizhou Univ, Sch Elect Informat & Elect Engn, Huizhou 516007, Peoples R China
2.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automation, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
5.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Peoples R China
推荐引用方式
GB/T 7714		 Lu, Yan-Feng,Yu, Qian,Gao, Jing-Wen,et al. Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection[J]. NEUROCOMPUTING,2022,513:70-82.
APA Lu, Yan-Feng,Yu, Qian,Gao, Jing-Wen,Li, Yi,Zou, Jun-Cheng,&Qiao, Hong.(2022).Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection.NEUROCOMPUTING,513,70-82.
MLA Lu, Yan-Feng,et al."Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection".NEUROCOMPUTING 513(2022):70-82.

入库方式： OAI收割

来源：自动化研究所

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