MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck

doi:10.1109/TIV.2022.3221767

	MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck
	Song, Ruiqi 1,2; Ai, Yunfeng 2,3; Tian, Bin1,2 ; Chen, Long1,2 ; Zhu, Fenghua1,2 ; Yao, Fei 4
发表期刊	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
ISSN	2379-8858
	2023-03-01
卷号	8 期号:3 页码:2285-2295
通讯作者	Ai, Yunfeng(aiyunfeng@ucas.ac.cn)
摘要	Accurate and reliable object detection is a fundamental component of perception system for autonomous driving. Specially, in some circumstances like autonomous driving in surface mine, there is a fact that the particularity of scene brings tremendous challenges for object detection with a series of problems caused by the multi-scale and camouflaged objects. In this paper, a multi-scale feature fusion and attention based multi-branches framework was proposed to improve the performance of object detection for above problems called MSFANet. In the proposed MSFANet, a multi-scale feature fusion module, which was used to capture the rich context features for multi-scale high level feature maps, and a multi-scale attention module, which was used to enhance the feature saliency of objects with different scales, were designed. What's more, to improve the performance of multi-scale object detection, we build 4 different prediction branches for large, medium small and smaller scale objects respectively. At last, we built our own dataset for automatic driving in surface mine called SurMine and test the model at our own datasets and KITTI benchmark. It achieved 82.7 mAP(%) and 92.57 mAP(%) in 32 36 ms on a TITAN RTX, compared to 80.2 mAP(%) and 87.83 mAP(%) in 28 similar to 34 ms by YOLOv7 on SurMine and KITTI benchmarks.
关键词	Feature extraction Object detection Autonomous vehicles Task analysis Detectors Semantics Real-time systems feature fusion object detection surface mine
DOI	10.1109/TIV.2022.3221767
关键词[WOS]	NEURAL-NETWORK ; VEHICLES
收录类别	SCI
语种	英语
资助项目	Key-Area Research and Development Program of Guangdong Province[2020B090921003] ; Key-Area Research and Development Program of Guangdong Province[2020B0909050001] ; Natural Science Foundation of Hebei Province[2021402011]
项目资助者	Key-Area Research and Development Program of Guangdong Province ; Natural Science Foundation of Hebei Province
WOS研究方向	Computer Science ; Engineering ; Transportation
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号	WOS:000981348100025
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：10[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/173211/53354
专题	多模态人工智能系统全国重点实验室
通讯作者	Ai, Yunfeng
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Waytous Inc, Qingdao 266109, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 4.North Automatic Control Technol Inst, Taiyuan 030006, Peoples R China
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Song, Ruiqi,Ai, Yunfeng,Tian, Bin,et al. MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2023,8(3):2285-2295.
APA	Song, Ruiqi,Ai, Yunfeng,Tian, Bin,Chen, Long,Zhu, Fenghua,&Yao, Fei.(2023).MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,8(3),2285-2295.
MLA	Song, Ruiqi,et al."MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 8.3(2023):2285-2295.