Research on Sensitive Image Detection Service Based on Deep Learning Framework

Abstract

In recent years, image detection services based on cloud computing deep learning have emerged at the historic moment, but due to the influence of network instability, bandwidth restrictions and many other factors, there may be a large response delay, which will seriously affect the user experience. How to allocate large-scale services to limited nodes, increase user satisfaction, and achieve the load balance, this has become a difficult problem to be solved at present. In this paper, the simulation environment is configured based on cloudsim, and the simulation experiments of standard particle swarm optimization algorithm and improved algorithm are carried out to simulate the scheduling strategy of sensitive image detection service suitable for the deep learning framework of this subject.