南京信息工程大学学报2025，Vol.17Issue(2)：172-180,9.DOI:10.13878/j.cnki.jnuist.20240927001
基于改进YOLOv8的桥梁裂缝无人机检测方法
Drone-based bridge crack detection based on improved YOLOv8
摘要
Abstract
To tackle the current challenges of low efficiency,poor performance,and inadequate real-time capabilities in bridge crack detection,this paper introduces a drone-based image detection method for bridge cracks using an improved YOLOv8 model.Firstly,the dynamic snake convolution kernel is integrated into the C2f module in the backbone of YOLOv8 to enhance the crack feature extraction.Then,the Context Augmentation Module(CAM)is introduced to improve the detection capability for small targets.Finally,the influence of low-quality datasets on de-tection results is reduced via optimizing the prediction box loss function.Experimental results show that the improved model achieves a GFLOPs of 14.4 and a mean Average Precision(mAP@50)of 94%,exhibiting a significant ac-curacy improvement compared to the baseline models.The detection speed reaches 147 frames per second,satisfying the requirements for real-time crack detection by UAVs.关键词
无人机图像/桥梁裂缝检测/YOLOv8/动态蛇形卷积/深度学习Key words
drone image/bridge crack detection/YOLOv8/dynamic snake convolution/deep learning分类
信息技术与安全科学引用本文复制引用
唐菲菲,杨浩,刘娜,姜敏,庞荣,张朋,周泽林..基于改进YOLOv8的桥梁裂缝无人机检测方法[J].南京信息工程大学学报,2025,17(2):172-180,9.基金项目
重庆市技术创新与应用发展专项重点项目(CSTB2022TIAD-KPX0098) （CSTB2022TIAD-KPX0098）
