Design of Digital Visual Transmission System Based on CAD Aided Technology and Deep Learning

Abstract

During the growth of AI (Artificial intelligence), DL (Deep learning) is a main research direction. This article deeply studies DL, and applies CAD technology and DL to the construction of digital visual transmission system. It constructs a neural network extraction optimization model based on image feature analysis. In the classification of image training models using network methods, deep learning is used to optimize image features. Improve the performance of DL network by optimizing model and parameters. Experimental research shows that the error of this algorithm is low, and the highest accuracy can reach 96.11%. Moreover, in the case of large parallel operation, the stability can reach about 89%, which shows that the system performance is excellent. Compared with the traditional decision tree algorithm, this method is more flexible, efficient, fast and practical. It provides theoretical and technical support for the application of CAD technology in the design of digital visual transmission system.