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
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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 StyleTao, 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
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
