A pixel-level deep segmentation network for automatic defect detection
文献类型：期刊论文
| 作者 | Yang, Lei2 ; Xu, Shuai; Fan, Junfeng1; Li, En1; Liu, Yanhong2,3
|
| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 出版日期 | 2023-04-01 |
| 卷号 | 215页码:11 |
| ISSN号 | 0957-4174 |
| 关键词 | Defect detection Deep convolutional neural network U-shape network ConvLSTM network |
| DOI | 10.1016/j.eswa.2022.119388 |
| 通讯作者 | Liu, Yanhong(liuyh@zzu.edu.cn) |
| 英文摘要 | 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. |
| WOS关键词 | INSPECTION ; RECONSTRUCTION ; SYSTEM |
| 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] |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| WOS记录号 | WOS:000911042200001 |
| 资助机构 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51072]  |
| 专题 | 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 |
| 通讯作者 | Liu, Yanhong |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst c, Beijing 100190, Peoples R China 2.Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.Zhengzhou Univ, Sch Elect & Informat Engn, Zhengzhou 450001, Henan, 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. |
入库方式： OAI收割
来源：自动化研究所
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