Pedestrian detection algorithm based on improved YOLOv3

The ability to accurately detect pedestrians in the area of interest in real time is crucial in the field of autonomous driving. An improved YOLOv3 model is proposed for pedestrian detection. Firstly, a lightweight model that incorporates a residual network module approach and a CBAM attention mechanism is added to the structure to enhance the feature representation capability of the network. Experimental results show that the improved YOLOv3 target detection model raises the detection accuracy by 4% compared to the original algorithm, and the accuracy precision is improved to a large extent, which verifies the feasibility and effectiveness of the improved YOLOv3 model for pedestrian detection.