Optimization of Image Recognition System for Civil Aviation Baggage Security Inspection Based on Artificial Intelligence

With the rapid development of aviation industry, baggage security inspection has become a vital link to ensure the safety of passengers. The purpose of this study is to optimize the image recognition system of civil aviation baggage security inspection by introducing Artificial Intelligence (AI) technology, and improve its accuracy, real-time performance and robustness. In this study, Faster R-CNN algorithm is adopted as the core target detection model, and a series of optimization methods are integrated into the system architecture, including data preprocessing, hardware optimization, interpretable algorithm and so on. Through the training of large-scale image data, the system successfully improves the accuracy of target recognition under different illumination and background conditions. In addition, by introducing image enhancement technology and confrontation training technology, the robustness of the system to different scenarios and potential attacks is enhanced. Secondly, through the introduction of parallel computing, hardware optimization and distributed system architecture, the system can realize real-time response in high-traffic airport scenes and meet the requirements of image recognition speed in practical applications. Our system can output the interpretation of the recognition result, which improves the security personnel’s trust in the system judgment. At the same time, the application of countermeasure training technology enhances the system’s resistance to potential attacks.