Research on the Application of Convolutional Neural Network Based on the YOLO Algorithm in Airport Intelligent Monitoring

With the technological development of civil aviation, the intelligent monitoring system based on security monitoring has gradually become a risk prevention and control mechanism in the daily operation of civil aviation. As the first line of defense for aviation safety, the airport has deployed a high-definition video surveillance system covering the entire area. However, the vast number of security surveillance videos makes it almost impossible to complete the monitoring and real-time tracking by manpower alone. In this context, the use of machine learning methods to identify risky clues in security surveillance videos and provide real-time early warnings has become the core implementation of intelligent airport surveillance. This paper discusses a smart monitoring system for airport security monitoring scenarios, designs the basic structure of the system, and builds a method for extracting and identifying potential illegal targets based on the YOLO algorithm. The research in this paper provides a basic technical framework for the airport intelligent monitoring platform and adequate technical support for the intelligent work of airport security.