Research on night pedestrian target recognition algorithm based on improved YOLOv5s
Aiming at the problems of slow speed,low precision and poor recognition effect in the process of traditional night-time pedestrian recognition,an improved YOLOv5s nighttime pedestrian recognition algorithm was proposed.Firstly,C3CSGC mod-ule was used to replace C3 module in the original YOLOv5s network model.Secondly,the loss function CIoU of YOLOv5s is re-placed by EIoU.Finally,the feature pyramid of YOLOv5s model is replaced by weighted bidirectional feature pyramid BiFPN.Ex-perimental results show that for the improved nighttime pedestrian recognition algorithm,the Precision(P)and Recall(R)of the original YOLOv5s model are increased by 4.1%and 5.9%,and the values of mAP_0.5 is increased by 7.2%,respectively.The num-ber of parameters changed from 7012825 to 3604758,and the model size changed from 14.4 M to 7.5M,indicating the effectiveness of the improved algorithm for nighttime pedestrian recognition.
deep learningpedestrian identification at nightYOLOv5sC3CSGCBiFPNloss function
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