Pedestrian detection method based on improved YOLOv5

Abstract

With the development of autonomous vehicles and intelligent transportation, more accurate detection of pedestrians. However, pedestrian detection suffers from occlusion and small target. First, the HorNet to improve the higher-order spatial interaction capability of the model, expand the effective sensory field, and enhance the feature extraction of pedestrians. Then, ODConv to gain the variability of each dimension and capture rich information. Finally, a layer to increase the accuracy of detecting pedestrians at small scales. We optimize the regression prediction of the anchor using the Efficient IOU Loss (EIOU) function. Experimental data show that the mean average precision (mAP) of the HOD-YOLOv5 model achieves 83.5%, compared to YOLOv5, which is 4.4% higher than original YOLOv5, and the recognition speed of the HOD-YOLOV5 reaches 106.7 frames per second (FPS). This demonstrates that the proposed model could realize real-time pedestrian detection at a relatively small cost, which satisfies the requirements of uncrewed and intelligent transportation.