Aiming at the problems of slow detection

low detection accuracy and poor robustness in pedestrian detection technology generated by overlapping occlusion between people in real life

based on the YOLOv3 network architecture

propose an improved YOLOv3 algorithm to realize network multi-layer feature multiplexing and fusion

and to amplify datasets and methods such as scale strategy train the network. The experimental results show that compared with the current mainstream target detection methods

this method improves the detection accuracy and recall rate of occluded pedestrians.