Construction of Intelligent Electronic Fence System Based on Computer Vision Algorithm

Abstract

With the continuous development of technology, electronic fences face more and more security issues and challenges. This paper used convolutional neural network (CNN) technology to establish an intrusion detection system to achieve high-precision recognition and real-time response to intrusion behavior. The system used image preprocessing technology to improve image quality and reduce environmental interference, and used multi-sensor information fusion to improve system robustness. In order to improve real-time response capabilities, the system uses multi-threaded design and model optimization to achieve rapid and accurate identification of safety hazards in complex environments. At the same time, the system also integrates functions such as behavior recognition and remote control to achieve automated intrusion defense and rapid response. The results show that the intelligent electronic fence system is superior to the traditional system in terms of response time, with an average response time of 109.1 milliseconds. The false alarm rate and missed alarm rate are significantly lower than those of the traditional system. The false alarm rate and missed alarm rate for flame detection are 0.7% and 0.1% respectively, and the detection range is superior to other systems under different conditions. The intelligent electronic fence system has significant advantages in improving security and protection capabilities, and provides a new technical solution for modern security protection.