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Article

Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks

1
Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(9), 1575; https://doi.org/10.3390/app8091575
Received: 13 August 2018 / Revised: 31 August 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
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. View Full-Text
Keywords: metallic surface; autoencoder; convolutional neural network; defect detection metallic surface; autoencoder; convolutional neural network; defect detection
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MDPI and ACS Style

Tao, X.; Zhang, D.; Ma, W.; Liu, X.; Xu, D. Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks. Appl. Sci. 2018, 8, 1575. https://doi.org/10.3390/app8091575

AMA Style

Tao X, Zhang D, Ma W, Liu X, Xu D. Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks. Applied Sciences. 2018; 8(9):1575. https://doi.org/10.3390/app8091575

Chicago/Turabian Style

Tao, Xian, Dapeng Zhang, Wenzhi Ma, Xilong Liu, and De Xu. 2018. "Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks" Applied Sciences 8, no. 9: 1575. https://doi.org/10.3390/app8091575

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