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Proceedings Paper

A fast button surface defects detection method based on convolutional neural network
Author(s): Lizhe Liu; Danhua Cao; Songlin Wu; Yubin Wu; Taoran Wei
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Paper Abstract

Considering the complexity of the button surface texture and the variety of buttons and defects, we propose a fast visual method for button surface defect detection, based on convolutional neural network (CNN). CNN has the ability to extract the essential features by training, avoiding designing complex feature operators adapted to different kinds of buttons, textures and defects. Firstly, we obtain the normalized button region and then use HOG-SVM method to identify the front and back side of the button. Finally, a convolutional neural network is developed to recognize the defects. Aiming at detecting the subtle defects, we propose a network structure with multiple feature channels input. To deal with the defects of different scales, we take a strategy of multi-scale image block detection. The experimental results show that our method is valid for a variety of buttons and able to recognize all kinds of defects that have occurred, including dent, crack, stain, hole, wrong paint and uneven. The detection rate exceeds 96%, which is much better than traditional methods based on SVM and methods based on template match. Our method can reach the speed of 5 fps on DSP based smart camera with 600 MHz frequency.

Paper Details

Date Published: 12 January 2018
PDF: 9 pages
Proc. SPIE 10621, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 1062107 (12 January 2018); doi: 10.1117/12.2294964
Show Author Affiliations
Lizhe Liu, Huazhong Univ. of Science and Technology (China)
Danhua Cao, Huazhong Univ. of Science and Technology (China)
Songlin Wu, Huazhong Univ. of Science and Technology (China)
Yubin Wu, Huazhong Univ. of Science and Technology (China)
Taoran Wei, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10621:
2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems
Jigui Zhu; Hwa-Yaw Tam; Kexin Xu; Hai Xiao; Liquan Dong, Editor(s)

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