| A pixel-level deep segmentation network for automatic defect detection | |
Yang, Lei2 ; Xu, Shuai; Fan, Junfeng3; Li, En3; Liu, Yanhong1,2
| |
| Source Publication | EXPERT SYSTEMS WITH APPLICATIONS
 |
| ISSN | 0957-4174 |
| 2023-04-01 | |
| Volume | 215Pages:11 |
| Corresponding Author | Liu, Yanhong(liuyh@zzu.edu.cn) |
| Abstract | 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. |
| Keyword | Defect detection Deep convolutional neural network U-shape network ConvLSTM network |
| DOI | 10.1016/j.eswa.2022.119388 |
| WOS Keyword | INSPECTION ; RECONSTRUCTION ; SYSTEM |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 Research Area | Computer Science ; Engineering ; Operations Research & Management Science |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science |
| WOS ID | WOS:000911042200001 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/51073 |
| Collection | 复杂系统管理与控制国家重点实验室_水下机器人 精密感知与控制研究中心_精密感知与控制 |
| Corresponding Author | Liu, Yanhong |
| Affiliation | 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 |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment