Pedestrian Detection Based on Modified YOLOv5  

作　　者：Ruopeng Pei 

机构地区：[1]Computer and Math Teaching Department,Shenyang Normal University Shenyang 110034,China

出　　处：《IJLAI Transactions on Science and Engineering》2025年第1期22-28,共7页IJLAI科学与工程学报汇刊(英文)

摘　　要：In the pedestrian detection scenario,the detection algorithm usually misses obscured and distant fuzzy pedestrians,and at the same time cannot take into account the detection accuracy and speed.In this paper,we propose a modified YOLOv5 model for pedestrian detection.Firstly,the backbone network uses the SPD-GCONV module constructed by the combination of SPD(Space-to-Depth)module and Ghost convolution for down-sampling to reduce the loss of fine-grained feature information.Secondly,the multi-scale detection ability of the model is enhanced by adding a small size detection layer.Then,the original CIoU loss function is replaced by α-EloU loss function to improve the accuracy of pedestrian target location.According to the experiments on WiderPerson data set,the average detection accuracy is improved by 2%compared with other pedestrian detection algorithms on the premise of ensuring the detection speed.Experimental results show that the improved algorithm can significantly improve the detection performance.

关 键 词：Pedestrian detection Space-to-Depth module Ghost convolution α-EloU 

分 类 号：G63[文化科学—教育学]