Abstract: Railway bridge monitoring is an important means to ensure transportation safety. To enhance the detection capability of existing monitoring systems for railway bridge personnel intrusion, this paper designed a personnel intrusion prevention system for railway bridge that included a video platform, an intelligent visual platform, and a business management platform. The paper adopted the YOLOv5 object detection model for personnel intrusion detection, and adopted multiple image data enhancement technologies to expand the training dataset, further improve the generalization ability and scene adaptation ability of the object detection model. The system was deployed and tested at the Yellow River Bridge in the Wannan Station area of Baoshen Railway Group Limited Liability Company. The experimental results show that the detection accuracy of the system for personnel intrusion is 95.3%, and the real-time detection performance is 2 ms. The accuracy and real-time performance of personnel intrusion detection meet the practical application requirements.