Research on improving pedestrian detection algorithm based on YOLOv5

Abstract

We proposed a pedestrian detection algorithm combining YOLOv5 with convolution and channel attention mechanism. First, we use our own pedestrian dataset to train the YOLOv5 detection model. Then, three attention mechanisms, SE, CBAMC3, and CoordAtt, are used to enhance the detection performance. The experiment demonstrated that the precision of both SE and CoordAtt decreased, the recall of SE also decreased, while the accuracy of CBAMC3 was improved and the mAP changed little, thus CBAMC3 became the best model for pedestrian detection. Research indicates that adding convolution block attention modules can increase the precision of detecting small pedestrian targets.