Author(s)

Chen Su

Corresponding Author

Chen Su

Abstract

This article introduces the core position of computer vision technology and the vital importance of DL (Deep Learning) in this field, and expounds the research background and importance of this study. Its goal is to deeply study the application of DL in computer vision system optimization and real-time identification technology, aiming at improving the performance of the system to meet the strict requirements of accuracy and real-time in practical applications. In this p•aper, a neural network architecture is innovatively constructed. Through careful design of model hierarchy, convolution kernel configuration and activation function, the representation and generalization ability of the model are enhanced. Subsequently, this article realizes the algorithm model, and optimizes it by means of parallel processing and model pruning, which effectively reduces the computational complexity and storage requirements. In order to test the effect of the algorithm, the experimental scheme is carefully designed, and the algorithm is comprehensively evaluated through comparative experiments. Experimental data show that the optimization algorithm proposed in this article has achieved significant improvement in accuracy, processing speed and storage occupation, which fully demonstrates the reliability of the algorithm model.

Keywords

Deep Learning; Computer Vision System; Real-Time Identification; Algorithm Optimization; Pattern Plan