ACR-Net: Attention Integrated and Cross-Spatial Feature Fused Rotation Network for Tubular Solder Joint Detection

doi:10.1109/TIM.2021.3094837

	ACR-Net: Attention Integrated and Cross-Spatial Feature Fused Rotation Network for Tubular Solder Joint Detection
	Zhou, Chenlin 1,2; Li, Daheng 1,2; Wang, Peng 1,2,3; Sun, Jia 1; Huang, Yikun 1; Li, Wanyi 1
刊名	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
	2021
卷号	70 页码:12
关键词	Deep learning defect detection object detection tubular solder joint detection
ISSN号	0018-9456
DOI	10.1109/TIM.2021.3094837
通讯作者	Wang, Peng(peng_wang@ia.ac.cn)
英文摘要	Tubular solder joint detection is an important and challenging issue in the industry, due to the illegible objects, rarely collected datasets and requiring high-precision and real-time performance for positioning and angle estimation. In this article, we propose an Attention integrated and Cross-spatial feature fused Rotation Network (ACR-Net) for tubular solder joint detection, which consists of a new feature extraction network named ECA-CSPDarknet44, a cross-spatial feature fusion network (CFFN), and a bin-based rotation detection network (BRDN). The proposed network can efficiently detect oriented tubular solder joints with high-precision and real-time performance. ECA-CSPDarknet44 with attention mechanism was presented, which can adaptively guide the network to learn important features of tubular solder joints, significantly improving the ability of feature extraction. By integrating multiscale global features and multiscale local region features, CFFN can enhance the network's ability to express the characteristics of tubular solder joints. Meanwhile, BRDN is proposed for oriented bounding box regression through a decoupling approach, which regresses target parameters efficiently and accurately with little increasing of model complexity. Finally, we establish a tubular solder joint dataset and conduct sufficient experiments to verify the effectiveness of our method. Our proposed ACR-Net achieves 98.8% mAP with 31.3 frames per second (FPSs) on the dataset, meeting the high-precision and real-time requirements of industrial systems.
资助项目	National Natural Science Foundation of China[91748131] ; National Natural Science Foundation of China[62006229] ; National Natural Science Foundation of China[61771471] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; Beijing Municipal Natural Science Foundation[4204113]
WOS关键词	INSPECTION ; SURFACE
WOS研究方向	Engineering ; Instruments & Instrumentation
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000688355400009
资助机构	National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science ; Beijing Municipal Natural Science Foundation
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/45872]
专题	智能机器人系统研究
通讯作者	Wang, Peng
作者单位	1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 3.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
推荐引用方式 GB/T 7714	Zhou, Chenlin,Li, Daheng,Wang, Peng,et al. ACR-Net: Attention Integrated and Cross-Spatial Feature Fused Rotation Network for Tubular Solder Joint Detection[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2021,70:12.
APA	Zhou, Chenlin,Li, Daheng,Wang, Peng,Sun, Jia,Huang, Yikun,&Li, Wanyi.(2021).ACR-Net: Attention Integrated and Cross-Spatial Feature Fused Rotation Network for Tubular Solder Joint Detection.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,70,12.
MLA	Zhou, Chenlin,et al."ACR-Net: Attention Integrated and Cross-Spatial Feature Fused Rotation Network for Tubular Solder Joint Detection".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70(2021):12.