广西师范大学学报（自然科学版）2025，Vol.43Issue(3)：84-97,14.DOI:10.16088/j.issn.1001-6600.2024071003
基于数据增广与改进YOLOv8的桥梁缺陷检测
Bridge Defect Detection Based on Data Augmentation and Improved YOLOv8
摘要
Abstract
In order to solve the problems of low detection accuracy,high missed detection rate and high false detection rate of bridge surface defects under the background of interference,a bridge defect detection method based on data enhancement and improved YOLOv8 is proposed.The small sample data is augmented by StyleGAN3 and depth image fusion.The SPD-Conv module is added to the YOLOv8 backbone to improve the feature extraction capability of low-resolution defects.Based on AFPN structure,AFPN_UCG structure is designed to make the network handle multi-scale information better.In C2f,RFCBAMConv and DLKA modules are introduced to construct C2f_RD module,which can transmit gradient information accurately and capture small target information more effectively.A new detection Head is designed by combining DCNv3 module with Dynamic Head,which combines three attention mechanisms of scale,space and task and uses DCNv3 dynamic adjustment to further improve the prediction performance of the model for irregular defects.Through experiments,mAP@0.5 increases by 2.4 percentage points after the data is expanded,and the accuracy rate of the improved YOLOv8 is 93.2%and mAP@0.5 is 91.3%,respectively,which are 4.2 and 4.3 percentage points higher than that of the original model,which can detect bridge defects more accurately.关键词
桥梁缺陷检测/StyleGAN3/YOLOv8/特征融合/注意力卷积/信息交互Key words
bridge defect detection/StyleGAN3/YOLOv8/feature fusion/convolution of attention/information interactive分类
计算机与自动化引用本文复制引用
梁胤杰,南新元,蔡鑫,李云鹏,勾海光..基于数据增广与改进YOLOv8的桥梁缺陷检测[J].广西师范大学学报（自然科学版）,2025,43(3):84-97,14.基金项目
国家自然科学基金(62303394) （62303394）
新疆维吾尔自治区自然科学基金(2022D01C694) （2022D01C694）
新疆维吾尔自治区高校基本科研业务费科研项目(XJEDU2023P025) （XJEDU2023P025）
