计算机与数字工程2025，Vol.53Issue(2)：444-450,7.DOI:10.3969/j.issn.1672-9722.2025.02.025
基于改进YOLOv5的轻量级落头目标检测方法
Lightweight Fallout Target Detection Method Based on Improved YOLOv5
张浩 1牛芳芳 2桑瑶烁2
作者信息
- 1. 安徽建筑大学电子与信息工程学院 合肥 230009||中国科学院合肥物质科学研究院 合肥 230031
- 2. 中国科学院合肥物质科学研究院 合肥 230031
- 折叠
摘要
Abstract
To address the equipment of accurate and fast detection of fallout,a lightweight fallout detection method based on improved YOLOv5 is proposed.Firstly,the Conv module and C3 module in Backbone and Neck are replaced by Ghost module to im-prove the detection speed of fallout.Secondly,a weighted bidirectional feature pyramid network BiFPN is added to the Neck part to improve the detection accuracy by better balancing the feature information of different scales through weighting.Finally,the GIoU loss function is replaced by α-IoU loss function to optimize the bounding box regression loss function to obtain more accurate bound-ing box localization accuracy.The experimental results show that the average accuracy of the improved YOLOv5 algorithm for fallout recognition reaches 96.1%,and the mAP0.5 and mAP0.5:0.95 reach 98.3%and 73.0%,respectively.Meanwhile,the model weight size is only 8.7 M,which can meet the requirements of accurate and fast detection of fallout.关键词
YOLOv5/落头检测/轻量化/BiFPN/损失函数Key words
YOLOv5/detection of fallout/lightweight/BiFPN/loss function分类
信息技术与安全科学引用本文复制引用
张浩,牛芳芳,桑瑶烁..基于改进YOLOv5的轻量级落头目标检测方法[J].计算机与数字工程,2025,53(2):444-450,7.
