An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection

doi:10.1109/TIM.2021.3127645

	An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection
	Yang, Lei 1,2; Song, Shouan 1,2; Fan, Junfeng3 ; Huo, Benyan 1,2; Li, En3 ; Liu, Yanhong 1,2
发表期刊	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
ISSN	0018-9456
	2022
卷号	71 页码:10
通讯作者	Liu, Yanhong(liuyh@zzu.edu.cn)
摘要	Accurate welding defect location is of great significance to modern manufacturing, which could be used for accurate quality evaluation and precise repairing decision-making basis of different products. Nevertheless, accurate welding defect location is still a challenging task due to some complex factors, such as complex backgrounds, low contrast, weak texture, and class imbalance issue. Recently, deep learning has got great development due to its strong feature expression ability, which has been widely applied into defect detection, but it still exists certain shortcomings on segmentation tasks with the class unbalanced issue or microdefects. To address these issues, with the encoder & x2013;decoder network architecture, a novel welding defect location method is proposed with an attention-guided segmentation network. To reduce the contextual information loss of the deep encoder module after multiple convolution and pooling operations, a multiscale feature fusion block is proposed to embed into a U-shaped network (U-Net) to acquire more information. On the basis, combined with a bidirectional convolutional long short-term memory (BiConvLSTM) block, an improved attention block is integrated into the skip connections between the encoder path and the decoder path to capture the global, long-range contexts and emphasize target regions, contributing to locate welding defect areas and enhance the segmentation ability on microdefects. Meanwhile, to address the foreground & x2013;background class imbalance issue, a hybrid loss function combined with binary cross-entropy (BCE) and loss functions is proposed to effectively utilize their unique excellent characteristics for accurate defect segmentation. Experiment results on the public GDXray dataset show that the proposed segmentation method could obtain a competitive segmentation performance compared with other advanced segmentation models.
关键词	Welding Feature extraction Image segmentation Inspection X-ray imaging Manufacturing Task analysis Attention model convolutional long short-term memory (ConvLSTM) defect detection feature fusion hybrid loss image segmentation
DOI	10.1109/TIM.2021.3127645
关键词[WOS]	INSPECTION SYSTEM ; MODEL
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62003309] ; National Key Research and Development Project of China[2020YFB1313701] ; Science and Technology Research Project in Henan Province of China[202102210098] ; Outstanding Foreign Scientist Support Project in Henan Province of China[GZS2019008]
项目资助者	National Natural Science Foundation of China ; National Key Research and Development Project of China ; Science and Technology Research Project in Henan Province of China ; Outstanding Foreign Scientist Support Project in Henan Province of China
WOS研究方向	Engineering ; Instruments & Instrumentation
WOS类目	Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000766300200007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：34[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/173211/48142
专题	复杂系统认知与决策实验室_水下机器人
通讯作者	Liu, Yanhong
作者单位	1.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China 2.Zhengzhou Univ, Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.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,Song, Shouan,Fan, Junfeng,et al. An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection[J]. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,2022,71:10.
APA	Yang, Lei,Song, Shouan,Fan, Junfeng,Huo, Benyan,Li, En,&Liu, Yanhong.(2022).An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection.IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT,71,10.
MLA	Yang, Lei,et al."An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection".IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 71(2022):10.