Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
Tao Xian; Zhang Dapeng; Ma Wenzhi; Liu Xilong; Xu De; Xian Tao
2018-09
发表期刊Applied Sciences-Basel
卷号8期号:9页码:1575
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.; 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.
关键词Metallic Surface Convolutional Neural Network Defect Detection
DOI10.3390/app8091575
收录类别SCI
引用统计
文献类型期刊论文
条目标识符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):1575.
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),1575.
MLA Tao Xian,et al."Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks".Applied Sciences-Basel 8.9(2018):1575.
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