现代电子技术2025，Vol.48Issue(13)：11-19,9.DOI:10.16652/j.issn.1004-373x.2025.13.002
基于YOLOv8n的轻量化道路裂缝检测算法
Lightweight road crack detection algorithm based on YOLOv8n
摘要
Abstract
In view of the wide object distribution scale,complex and diverse features and the demand of dealing with a large number of datasets in automatic road crack detection,a lightweight road crack detection algorithm GCW-YOLO based on YOLOv8n is proposed.Firstly,the global attention mechanism is introduced into the backbone network to enhance the ability to extract and fuse road crack features first,and then the original loss function is replaced with Wise-IoU loss function to get better feature focus and reduce the loss of features and classification in prediction.Finally,the lightweight network structure GhostNet is introduced into the C2f module to improve the feature extraction efficiency of the model and reduce the computational complexity.Experiments were conducted on a self-made expressway crack disease dataset with a total of 15 116 images,and the generalization performance of the algorithm was verified on public datasets.Experimental results show that the mean average precision(mAP)of the proposed algorithm reaches 63.5%,which is improved by 6.0%in comparison with that of the original model,its spatial and temporal efficiency is improved by 3.0%and 8.5%,respectively,and its detection speed reaches 250 f/s.The comparative experimental results show that the GCW-YOLO algorithm combines the advantages of lightweight and high detection accuracy,and shows good generalization,so it has good practical value and promotion prospect in road maintenance.关键词
道路裂缝检测/深度学习/YOLOv8n/注意力机制/轻量化/特征聚焦Key words
road crack detection/deep learning/YOLOv8n/attention mechanism/lightweighting/feature focus分类
电子信息工程引用本文复制引用
吐尔逊·买买提,邱建卓,朱兴林,徐粒..基于YOLOv8n的轻量化道路裂缝检测算法[J].现代电子技术,2025,48(13):11-19,9.基金项目
国家自然科学基金项目(51768071) （51768071）
新疆交通投资(集团)有限责任公司科技项目:基于沥青路面病害数据及深度学习的病害智慧识别系统研究(JCZXXJAU2023001) （集团）
