Defect Detection of Mobile Phone Surface Based on Convolution Neural Network

Abstract

Automatic surface defect detection of mobile phone in large scale needs to process high resolution images and handle various defects while achieving high accuracy rate. This study proposes a defect detection method based on convolution neural network (CNN). Firstly, the original surface image of mobile phone is obtained using industrial linear array camera. Secondly, the obtained images are automatically segmented into specified sizes by the proposed preprocessing step. Moreover, we design the CNN on basis of GoogLeNet network, which greatly reduces the number of parameters without compromising prediction rate. At last the designed CNN are trained and tested. The trained CNN can be combined with a sliding window technique to detect any ROI with size larger than 256×256 resolutions in the original images. The experimental results show that the defect detection rate of the designed CNN can achieve as high as 99.5%.