A fast button surface defects detection method based on convolutional neural network

Lizhe Liu; Danhua Cao; Songlin Wu; Yubin Wu; Taoran Wei

doi:10.1117/12.2294964

12 January 2018 A fast button surface defects detection method based on convolutional neural network

Lizhe Liu, Danhua Cao, Songlin Wu, Yubin Wu, Taoran Wei

Proceedings Volume 10621, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems; 1062107 (2018) https://doi.org/10.1117/12.2294964
Event: International Conference on Optical Instruments and Technology 2017, 2017, Beijing, China

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.

Citation Download Citation

Lizhe Liu, Danhua Cao, Songlin Wu, Yubin Wu, and Taoran Wei "A fast button surface defects detection method based on convolutional neural network", Proc. SPIE 10621, 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, 1062107 (12 January 2018); https://doi.org/10.1117/12.2294964

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available