Pedestrian object detection algorithm based on lightweight YOLOv7 in complex street scenarios

Abstract

In view of the problems of excessive parameter setting and large calculation of YOLOv7 in pedestrian object detection in complex street scenarios, this paper proposes a lightweight method to improve YOLOv7 algorithm. Under the YOLOv7 framework, Partial Convolution (PConv) is integrated into the convolution of the original algorithm, replacing part of the convolution in the original convolution layer, and the SEAttention attention module is introduced to ensure the detection accuracy of the lightweight algorithm. The experimental results on the home-made data set show that, compared with the original YOLOv7 algorithm, the number of model parameters decreased by 11.0% in the improved YOLOv7 algorithm, and the algorithm calculation volume decreased by 19.4%, while ensuring the high accuracy of the original YOLOv7 algorithm. In this paper, the algorithm reduces the number of parameters and calculations, and achieves the balance of lightweight and accuracy.