An improved depth learning method for surface defect detection

In order to solve the problems of low recognition rate, incapability of autonomous detection and weak generality of the existing surface defect detection methods, an improved depth learning surface defect detection method is proposed. This method improves the convolution neural network model in depth learning and divides it into two modules: segmentation module and decision module. After preprocessing, the image is input to the segmentation module for training, and then the output of the segmentation module and network features are used as input to the decision module to detect defects in the image. In the improved model, the convolution layer and convolution kernel size in the segmentation module are optimized, and a new convolution network model is constructed. In downsampling, the maximum pool is used instead of the maximum stride, and the loss function and activation function are designed at the same time. Experiments show that the method has a high defect detection accuracy rate of 99%, realizes autonomous detection, and has certain universality.