Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
文献类型：期刊论文
| 作者 | Tao Xian ; Zhang Dapeng; Ma Wenzhi; Liu Xilong; Xu De; Xian Tao
|
| 刊名 | APPLIED SCIENCES-BASEL
 |
| 出版日期 | 2018-09-01 |
| 卷号 | 8期号:9页码:15 |
| 关键词 | metallic surface autoencoder convolutional neural network defect detection |
| ISSN号 | 2076-3417 |
| DOI | 10.3390/app8091575 |
| 通讯作者 | Tao, Xian(taoxian2013@ia.ac.cn) |
| 英文摘要 | Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications. |
| WOS关键词 | FAULT-DIAGNOSIS ; INSPECTION ; CLASSIFICATION ; IMAGES ; MODEL |
| 资助项目 | Science Challenge Project[TZ2018006-0204-02] ; National Natural Science Foundation of China[61703399] ; National Natural Science Foundation of China[61503376] ; National Natural Science Foundation of China[61673383] |
| WOS研究方向 | Chemistry ; Materials Science ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000445760200164 |
| 出版者 | MDPI |
| 资助机构 | Science Challenge Project ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21696]  |
| 专题 | 精密感知与控制研究中心_精密感知与控制 |
| 通讯作者 | Xian Tao |
| 作者单位 | Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Tao Xian,Zhang Dapeng,Ma Wenzhi,et al. Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks[J]. APPLIED SCIENCES-BASEL,2018,8(9):15. |
| APA | Tao Xian,Zhang Dapeng,Ma Wenzhi,Liu Xilong,Xu De,&Xian Tao.(2018).Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks.APPLIED SCIENCES-BASEL,8(9),15. |
| MLA | Tao Xian,et al."Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks".APPLIED SCIENCES-BASEL 8.9(2018):15. |
入库方式： OAI收割
来源：自动化研究所
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