Visual measurement of milling surface roughness based on Xception model with convolutional neural network

Abstract

At present, machine vision roughness detection mostly needs to design roughness related indexes based on images, and the index design has human intervention and is heavily dependent on the light source environment. To solve this problem, the paper classifies the surface roughness based on the deep convolutional neural network method, which can realize the roughness detection without index design. The most important thing is that the detection method has good light source robustness under different light source environments. The study adopts an end-to-end image analysis method, by means of image enhancement pre-processing of a small number of source images, after multi-layer convolution and pooling operations, as well as comprehensive processing of fully connected and classification layers, the convolutional kernel can automatically extract the features of the image, and finally the surface roughness obtained by vertical disc cutter milling can be classified and predicted. In addition, based on the experimental light source environment with two different luminance of night and day, by comparing with the technically mature ResNet50 and DenseNet121 convolutional neural network models, the deep convolutional neural network Xception model not only has high roughness classification accuracy, but also has more light source environment robustness. This method makes the online measurement of visual roughness possible.