A pixel-level deep segmentation network for automatic defect detection

doi:10.1016/j.eswa.2022.119388

	A pixel-level deep segmentation network for automatic defect detection
	Yang, Lei2 ; Xu, Shuai ; Fan, Junfeng3 ; Li, En3 ; Liu, Yanhong1,2
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
	2023-04-01
卷号	215 页码:11
通讯作者	Liu, Yanhong(liuyh@zzu.edu.cn)
摘要	Defect detection is a very important link for much manufacturing and processing applications which could be used for quality control and precise maintenance decision. However, faced with the weak-texture and low-contrast industrial environment, high-precision defect detection still faces a certain challenge due to diverse and complex of defects. Meanwhile, due to a minimal portion image pixels of defects, the pixel-level defect detection task is always against class-unbalance issue which also will affect the detection performance. Recently, with the strong automatic feature representation ability, deep learning has shown an excellent detection performance on defect identification and location. Nevertheless, it still has some demerits, such as insufficient processing of feature maps, lack of temporal modeling information, etc. To address these issues, on the basis of the encoder-decoder architecture, a pixel-level deep segmentation network is proposed for automatic defect detection to construct an end-to-end defect segmentation model. To realize effective feature representation, a residual attention network is proposed to construct the backbone network, which could also make the segmentation network better emphasize target regions. Meanwhile, to improve the network propagation ability of subtle context features, a bidirectional convolutional long short-term memory (ConvLSTM) block is introduced to optimize the skip connections to learn long-range spatial contexts. Besides, a weighted loss function is proposed for model training to address the class-unbalance issue. Combined with multiple public data sets, through qualitative and quantitative analysis, experimental results demonstrate that the proposed defect segmentation network achieves a better performance compared to other state-of-the-art segmentation methods.
关键词	Defect detection Deep convolutional neural network U-shape network ConvLSTM network
DOI	10.1016/j.eswa.2022.119388
关键词[WOS]	INSPECTION ; RECONSTRUCTION ; SYSTEM
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62003309] ; National Key Research & Development Project of China[2020YFB1313701] ; Science & Technology Research Project in Henan Province of China[212102210010] ; 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 & Development Project of China ; Science & Technology Research Project in Henan Province of China ; Outstanding Foreign Scientist Support Project in Henan Province of China
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000911042200001
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：5[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/173211/51073
专题	复杂系统认知与决策实验室_水下机器人中科院工业视觉智能装备工程实验室_精密感知与控制
通讯作者	Liu, Yanhong
作者单位	1.Zhengzhou Univ, Sch Elect & Informat Engn, Zhengzhou 450001, Henan, Peoples R China 2.Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst c, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Yang, Lei,Xu, Shuai,Fan, Junfeng,et al. A pixel-level deep segmentation network for automatic defect detection[J]. EXPERT SYSTEMS WITH APPLICATIONS,2023,215:11.
APA	Yang, Lei,Xu, Shuai,Fan, Junfeng,Li, En,&Liu, Yanhong.(2023).A pixel-level deep segmentation network for automatic defect detection.EXPERT SYSTEMS WITH APPLICATIONS,215,11.
MLA	Yang, Lei,et al."A pixel-level deep segmentation network for automatic defect detection".EXPERT SYSTEMS WITH APPLICATIONS 215(2023):11.