Fast Pedestrian Detection Algorithm Based on Improved YOLOv3

Abstract

Aiming at the problems of fast-moving speed, easy occlusion, and complex background of pedestrians in traffic scenes, a fast pedestrian detection algorithm based on improved YOLOv3 is proposed. First, choose the efficient lightweight network ShuffleNetv2 to replace the original backbone network Darknet-53 to reduce the model complexity and improve the detection speed. Second, a reverse residual structure is introduced in the detection network layer to enhance the expressiveness of features. Third, a coordinate attention mechanism is introduced to suppress useless information and enhance the network's ability to focus on key features. Fourth, the spatial pyramid pooling structure is introduced to realize multi-scale feature fusion of the network and improve the detection accuracy of small objects. The experimental results show that compared with YOLOv3, the improved YOLOv3 proposed in this paper can improve the detection accuracy and detection speed by 0.7% and 53.8% respectively, which is more conducive to the rapid detection of pedestrians.