Research on Computer Vision Image Classification System Based on Deep Learning

This paper uses Python and TensorFlow framework. Using low power computer Raspberry as model computing platform and sorting processing platform, the damaged parts identification system based on computer vision technology and deep learning technology is realized. Multi-scale interactive iterative learning algorithm is studied. This method encodes the dense features of each pixel, and realizes the effective capture of key information such as texture, color and edge of each pixel through multi-scale cyclic learning. Edge extraction of objects is carried out to complete the final semantic understanding. CNN algorithm mainly extracts dense features from original images, constructs convolutional networks, and obtains prior information through training large-scale samples. The system adopts the advanced mobile CNN mode, which has the characteristics of simple structure, reliable and easy maintenance, and can identify and classify the damaged parts quickly and efficiently.