Abstract: This paper proposed a visual safety detection method based on improved YOLOv8 personnel detection model to address the issue of personnel intrusion that affected the operational safety for intelligent self wheel operation and maintenance equipment vehicle sets (referred to as operation and maintenance vehicle sets) of railway catenary system. Based on the YOLOv8 model and introduced FasterNet Block and Efficient Multi Scale Attention Module (EMA), the paper learned cross spatial aggregated pixel features, focused on single model missed detection and performed head target detection and body instance segmentation separately in the same scene, fusing two recognition boxes to obtain accurate recognition results, and used coordinate transformation method to implement precise personnel positioning, determine the distance between people and vehicles, and classify the danger level. This method implements the entire process of "recognition - positioning - warning" and can be applied to intelligent operation and maintenance vehicle sets. By detecting and locating personnel in the work area, it improves the safety of intelligent operation and maintenance vehicle sets.