Patrol duty is a security and stability maintenance operation of great significance， but the patrol environment is complex， the object are diverse， and the problem of difficult detection is very prominent， so how to accurately and real-time detect patrol duty objects is of great practical significance.In order to improve the accuracy and real-time detection of patrol duty objects， the YOLOv5 algorithm is improved. In order to suppress the interference caused by the patrol environment， the ECA-Net attention mechanism is combined to improve the saliency of the detected object； and the introduction of BiFPN structure ensures better real-time performance and multi-scale object detection capabilities of the algorithm.Comparing the improved algorithm with the original algorithm， the mAP is improved by 3.51% ； comparing with four algorithms， the results show that the algorithm can better reduce the impact of patrol object detection of due to the problems of similar detection，diverse scales and light interference， which further verifies the effectiveness of the proposed algorithm in the task of patrol duty object detection.