Research on pedestrian targe detection based on deep learning

Abstract

In the process of autonomous driving, there will be missed detections and false detections caused by dense crowds and occlusions during pedestrian target detection. This paper proposes a pedestrian object detection network model that combines Swin Transformer and YOLOv3. First use the lightweight Swin Transformer Tiny to replace the original Darknet53 as the backbone network of YOLOv3. The multi-scale detection is realized through the self-attention hierarchical network, which optimizes the detection effect in the case of dense pedestrians. Secondly, to deal with the occlusion in the crowd, Focal-EIoU Loss is used as a new loss function. I Introduce edge length loss and Focal L1 loss to increase the loss and gradient of IoU, thereby improving the regression accuracy. Finally, experiments are performed on the Caltech dataset. The experimental results show that the precision on the Caltech dataset reaches 95.23% and the recall rate reaches 89.57%. Compared with the original YOLOv3 algorithm, the precision is increased by 3.22%, and the recall rate is increased by 4.35%. The effectiveness of the algorithm is verified, and the performance of pedestrian detection is greatly improved.