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

doi:10.1016/j.neucom.2022.09.117

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Cross stage partial connections based weighted Bi-directional feature pyramid and enhanced spatial transformation network for robust object detection
	Lu, Yan-Feng1,2,5 ; Yu, Qian 1,2,5; Gao, Jing-Wen 1,2,5; Li, Yi3 ; Zou, Jun-Cheng4 ; Qiao, Hong1,2,5
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
	2022-11-07
卷号	513 页码:70-82
通讯作者	Lu, Yan-Feng(yanfeng.lv@ia.ac.cn)
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.
关键词	Robust object detection Structural deformation Image detection Spatial transformation
DOI	10.1016/j.neucom.2022.09.117
关键词[WOS]	ALIGNMENT
收录类别	SCI
语种	英语
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]
项目资助者	Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; National Natural Science Foundation of China
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000866415400007
出版者	ELSEVIER
引用统计	被引频次：3[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50279
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Lu, Yan-Feng
作者单位	1.Chinese Acad Sci, Inst Automation, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Nanchang Univ, Sch Informat Engn, Nanchang 330031, Peoples R China 4.Huizhou Univ, Sch Elect Informat & Elect Engn, Huizhou 516007, Peoples R China 5.Chinese Acad Sci, Inst Automation, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
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.