水力发电学报2025，Vol.44Issue(10)：48-58,11.DOI:10.11660/slfdxb.20251005
改进的YOLOv8n模型在大坝裂缝检测中的应用研究
Study on application of improved YOLOv8n model in dam crack detection
摘要
Abstract
This study presents an improved YOLOv8n-based detection method to address the issue of false detections of dam cracks that is caused by low-quality surveillance images,limited effective samples,and interference from complex backgrounds.This model is trained using a dataset comprising 193 real-world crack images featuring complex engineering backgrounds,and enhanced by modifying the mosaic data augmentation mechanism and incorporating negative sample training targeted at the objects that were often falsely detected.Numerical experiments demonstrate that under small-sample training conditions,the YOLOv8n model achieves a mean Average Precision(mAP)of 89.2％,meeting the requirements of general engineering applications.After negative sample training,the mAP increases to 92.5％,and the false detection rate is reduced by 10.1％,providing an effective solution to the false detection problem in complex background scenarios.Our findings indicate that the YOLOv8n model is well-suited for dam surveillance images of suboptimal quality,and that the negative sample training strategy significantly improves detection accuracy.This approach offers a novel solution to crack identification in hydraulic projects,practically significant for engineering applications.关键词
水利工程/YOLOv8/负样本训练/大坝裂缝检测/水利工程安全监测Key words
hydro-engineering/YOLOv8/negative sample training/dam crack detection/water conservancy project safety monitoring分类
建筑与水利引用本文复制引用
薛文博,齐慧君,尹广林,吴志伟,李同春..改进的YOLOv8n模型在大坝裂缝检测中的应用研究[J].水力发电学报,2025,44(10):48-58,11.基金项目
中央高校基本科研业务费专项资金资助(B240201025) （B240201025）
