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

doi:10.1109/TIM.2021.3127645

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 > 水下机器人

	An Automatic Deep Segmentation Network for Pixel-Level Welding Defect Detection
	Yang, Lei 1,3; Song, Shouan 1,3; Fan, Junfeng 2; Huo, Benyan 1,3; Li, En 2; Liu, Yanhong 1,3
刊名	IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
	2022
卷号	71 页码:10
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
ISSN号	0018-9456
DOI	10.1109/TIM.2021.3127645
通讯作者	Liu, Yanhong(liuyh@zzu.edu.cn)
英文摘要	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.
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]
WOS关键词	INSPECTION SYSTEM ; MODEL
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:000766300200007
资助机构	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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48141]
专题	复杂系统管理与控制国家重点实验室_水下机器人
通讯作者	Liu, Yanhong
作者单位	1.Zhengzhou Univ, Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, 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.