湖北汽车工业学院学报2025，Vol.39Issue(2)：1-6,18,7.DOI:10.3969/j.issn.1008-5483.2025.02.001
基于无人机检测的轻量化YOLOv8路面病害检测算法
An UAV-based Lightweight YOLOv8 Algorithm for Road Disease Detection
摘要
Abstract
To meet the real-time and accuracy requirements of unmanned aerial vehicle(UAV)devices in delecting road disease,a lightweight detection algorithm based on an improved YOLOv8(LHP-YO-LO)was proposed.The RG-C2f module replaced the original C2f module to reduce computational re-dundancy,and a lightweight detection head Detect-T3G was designed to reduce the model's parameter count.In addition,a content-guided attention fusion mechanism was embedded in the neck network to enhance target detection accuracy in complex backgrounds.Channel-wise knowledge distillation was applied to compensate for the accuracy loss caused by lightweight optimization.Experiments on the road disease dataset RDD 2022 demonstrate that the improved model achieves a 3.4%increase in mAP50 compared to the original model,with parameter count and computational load reaching 1.76×106 and 3.9 GFLOPs,respectively,representing reductions of 41.3%and 51.8%compared to the origi-nal model.The improved model meets the real-time requirements for road disease detection UAVs.关键词
无人机/路面病害/目标检测/YOLOv8/轻量级/知识蒸馏Key words
UAV/road disease/target detection/YOLOv8/lightweight/knowledge distillation分类
计算机与自动化引用本文复制引用
郭锦波,王生怀,陈晓辉,王宸,张伟..基于无人机检测的轻量化YOLOv8路面病害检测算法[J].湖北汽车工业学院学报,2025,39(2):1-6,18,7.基金项目
国家自然科学基金(52475557) （52475557）
湖北省重点研发计划项目(2021BAA056) （2021BAA056）
湖北省自然科学基金(2020CFB755) （2020CFB755）
湖北省教育厅重点项目(D20231806) （D20231806）
湖北省高等学校优秀中青年科技创新团队计划项目(T2020018) （T2020018）
