Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures
文献类型：期刊论文
| 作者 | Wei, Jing2,3; Zhang, Zhengtao1,2,3 ; Shen, Fei1,2,3; Lv, Chengkan1,2,3
|
| 刊名 | MACHINES
 |
| 出版日期 | 2022-12-01 |
| 卷号 | 10期号:12页码:17 |
| 关键词 | industrial manufacturing deep learning data augmentation defect generation defect detection |
| DOI | 10.3390/machines10121239 |
| 通讯作者 | Lv, Chengkan(chengkan.lv@ia.ac.cn) |
| 英文摘要 | Defect generation is a crucial method for solving data problems in industrial defect detection. However, the current defect generation methods suffer from the problems of background information loss, insufficient consideration of complex defects, and lack of accurate annotations, which limits their application in defect segmentation tasks. To tackle these problems, we proposed a mask-guided background-preserving defect generation method, MDGAN (mask-guided defect generation adversarial networks). First, to preserve the normal background and provide accurate annotations for the generated defect samples, we proposed a background replacement module (BRM), to add real background information to the generator and guide the generator to only focus on the generation of defect content in specified regions. Second, to guarantee the quality of the generated complex texture defects, we proposed a double discrimination module (DDM), to assist the discriminator in measuring the realism of the input image and distinguishing whether or not the defects were distributed at specified locations. The experimental results on metal, fabric, and plastic products showed that MDGAN could generate diversified and high-quality defect samples, demonstrating an improvement in detection over the traditional augmented samples. In addition, MDGAN can transfer defects between datasets with similar defect contents, thus achieving zero-shot defect detection. |
| 资助项目 | Youth Innovation Promotion Association, CAS ; [2020139] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000900844200001 |
| 出版者 | MDPI |
| 资助机构 | Youth Innovation Promotion Association, CAS |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51314]  |
| 专题 | 精密感知与控制研究中心_精密感知与控制 |
| 通讯作者 | Lv, Chengkan |
| 作者单位 | 1.CASI Vis Technol Co Ltd, Luoyang 471000, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wei, Jing,Zhang, Zhengtao,Shen, Fei,et al. Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures[J]. MACHINES,2022,10(12):17. |
| APA | Wei, Jing,Zhang, Zhengtao,Shen, Fei,&Lv, Chengkan.(2022).Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures.MACHINES,10(12),17. |
| MLA | Wei, Jing,et al."Mask-Guided Generation Method for Industrial Defect Images with Non-uniform Structures".MACHINES 10.12(2022):17. |
入库方式： OAI收割
来源：自动化研究所
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