A surface defect detection based on convolutional neural network

Abstract

Surface defect detection is a common task in industry production. Generally, designer has to find out a suitable feature to separate defects in the image. The hand-designed feature always changes with different surface properties which lead to weak ability in other datasets. In this paper, we firstly present a general detecting method based on convolutional neural network (CNN) to overcome the common shortcoming. CNN is used to complete image patch classification. And features are automatically exacted in this part. Then, we build a voting mechanism to do a final classification and location. The good performances obtained in both arbitrary textured images and special structure images prove that our algorithm is better than traditional case-by-case detection one. Subsequently, we accelerate algorithm in order to achieve real-time requirements. Finally, multiple scale detection is proposed to get a more detailed locating boundary and a higher accuracy.