Automatic Detection and Location of Weld Beads With Deep Convolutional Neural Networks

doi:10.1109/TIM.2020.3026514

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Automatic Detection and Location of Weld Beads With Deep Convolutional Neural Networks
	Yang, Lei 1; Fan, Junfeng 2; Liu, Yanhong 1; Li, En 2; Peng, Jinzhu 1; Liang, Zize 2
刊名	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
	2021
卷号	70 页码:12
关键词	Data augmentation deep convolutional network model object location samples updating semantic segmentation weld bead
ISSN号	0018-9456
DOI	10.1109/TIM.2020.3026514
通讯作者	Yang, Lei(liuyh@zzu.edu.cn)
英文摘要	Welding quality detection is a critical link in modern manufacturing, and the weld bead location is a prerequisite for the high-precision assessment of welding quality. It is generally necessary for weld bead detection to be accomplished in the context of complex industrial environments. However, conventional detection and location methods based on specific detection conditions or prior knowledge lack accuracy and adaptability. To precisely detect and locate the weld beads in real industrial environments, a novel weld bead detection and location algorithm is proposed based on deep convolutional neural networks. Because there is no open data set of weld beads and the samples in real industrial applications are insufficient for effective model training of the deep convolutional neural network, a novel data augmentation method based on a deep semantic segmentation network is proposed to increase the sample diversity and enlarge the data set. Then, a dynamic sample updating strategy is put forward to cover more welding situations. Finally, faced with the weak-feature and weak-texture characteristics of weld beads, a simplified YOLOV3 model is proposed to realize end-to-end weld bead location. Experiments demonstrate that the proposed method could effectively satisfy the robustness and precision requirements for weld bead detection and location combined with a deep semantic segmentation network and simplified YOLOV3 model.
资助项目	National Natural Science Foundation of China[61473265] ; National Natural Science Foundation of China[61803344] ; National Natural Science Foundation of China[61773351] ; Science & Technology Research Project in Henan Province[202102210098] ; Outstanding Foreign Scientist Support Project in Henan Province[GZS2019008] ; Innovation Research Team of Science & Technology in Henan Province of China[17IRTSTHN013]
WOS关键词	DEFECT DETECTION ; SEAM TRACKING ; AL-ALLOY ; LASER ; SYSTEM
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:000594910700008
资助机构	National Natural Science Foundation of China ; Science & Technology Research Project in Henan Province ; Outstanding Foreign Scientist Support Project in Henan Province ; Innovation Research Team of Science & Technology in Henan Province of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/42789]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Yang, Lei
作者单位	1.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lei,Fan, Junfeng,Liu, Yanhong,et al. Automatic Detection and Location of Weld Beads With Deep Convolutional Neural Networks[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2021,70:12.
APA	Yang, Lei,Fan, Junfeng,Liu, Yanhong,Li, En,Peng, Jinzhu,&Liang, Zize.(2021).Automatic Detection and Location of Weld Beads With Deep Convolutional Neural Networks.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,70,12.
MLA	Yang, Lei,et al."Automatic Detection and Location of Weld Beads With Deep Convolutional Neural Networks".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 70(2021):12.