现代信息科技2025，Vol.9Issue(2)：33-37,45,6.DOI:10.19850/j.cnki.2096-4706.2025.02.006
基于YOLOv5的行人检测系统研究
Research on Pedestrian Detection System Based on YOLOv5
摘要
Abstract
Aiming at problems of the amount of calculation with redundancy and low accuracy,an improved YOLOv5 pedestrian detection model is proposed.This system uses Ghost convolution to combine with the Transformer Self-Attention Mechanism,then combines with the BiFPN structure and EIoU loss function.The INRIA pedestrian detection data set is divided into the training set,the validation set,and the test set according to the ratio of 7∶2∶1.The SGD optimizer is used to train the model for 300 Epochs,and the trained weight model is used to test the test set.The results show that the average accuracy of the improved model detection is increased by 1.5%,and the amount of calculation is significantly reduced.关键词
行人检测/YOLOv5模型/Ghost卷积/双向金字塔结构Key words
pedestrian detection/YOLOv5 model/Ghost convolution/BiFPN分类
信息技术与安全科学引用本文复制引用
焦天文,田秀云..基于YOLOv5的行人检测系统研究[J].现代信息科技,2025,9(2):33-37,45,6.基金项目
2022年广东海洋大学校级课程-数学物理方法(010301112202) （010301112202）
