Application Research of Image Recognition Algorithm Based on Deep Learning in Intelligent Monitoring System

With the extensive implementation of smart surveillance systems in the security domain, enhancing image recognition accuracy and efficiency has emerged as a crucial challenge. In this research, an image identification algorithm rooted in deep learning is proposed to boost the object detection and recognition capabilities of intelligent monitoring systems. During the system simulation phase, a convolutional neural network (CNN) model is established to train and test a substantial volume of surveillance image data. Comparative experiments validate the superior performance of the proposed method in complex environments. The findings demonstrate that this approach can greatly enhance the monitoring system’s target identification precision across different scenarios while maintaining excellent processing speed. The in-depth analysis of simulation data further confirms the reliability and practicality of the system, offering technical backing for the evolution of intelligent monitoring systems. This work not only details the algorithm design process but also validates the simulation outcomes through precise data analysis, indicating that this algorithm’s application in smart surveillance systems holds broad prospects and significant practical value.