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

doi:10.1109/TIV.2022.3221767

CORC > 自动化研究所 > 中国科学院自动化研究所 > 多模态人工智能系统全国重点实验室

	MSFANet: A Light Weight Object Detector Based on Context Aggregation and Attention Mechanism for Autonomous Mining Truck
	Song, Ruiqi 1,2; Ai, Yunfeng 1,4; Tian, Bin 1,2; Chen, Long 1,2; Zhu, Fenghua 1,2; Yao, Fei 3
刊名	IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
	2023-03-01
卷号	8 期号:3 页码:2285-2295
关键词	Feature extraction Object detection Autonomous vehicles Task analysis Detectors Semantics Real-time systems feature fusion object detection surface mine
ISSN号	2379-8858
DOI	10.1109/TIV.2022.3221767
通讯作者	Ai, Yunfeng(aiyunfeng@ucas.ac.cn)
英文摘要	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.
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]
WOS关键词	NEURAL-NETWORK ; VEHICLES
WOS研究方向	Computer Science ; Engineering ; Transportation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000981348100025
资助机构	Key-Area Research and Development Program of Guangdong Province ; Natural Science Foundation of Hebei Province
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/53353]
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.Waytous Inc, Qingdao 266109, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.North Automatic Control Technol Inst, Taiyuan 030006, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
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.