华南理工大学学报（自然科学版）2026，Vol.54Issue(6)：100-109,10.DOI:10.12141/j.issn.1000-565X.250356
改进YOLOv11的无人机航拍公路坑槽检测算法
Improved YOLOv11 Algorithm for Highway Potholes Detection in Aerial Images by UAV
摘要
Abstract
Addressing the challenges of significant multi-scale variations in target objects and insufficient detection accuracy caused by complex scenarios in UVA aerial image-based pothole detection on roads,this paper proposes an improved YOLOv11 algorithm for pothole detection in UVA-captured images.Firstly,in the backbone network,the original C3K2 features extraction module is replaced with a lightweight enhanced detection module(LEDM).By using a grouped parallel processing method,this module segments multi-scale pothole feature channels and dynamically extracts key pothole information through adaptive feature enhancement.It combines lightweight computation with the elimination of redundant parameters to improve both the accuracy of pothole feature extraction and the operational efficiency of the model.Secondly,in the neck of the network,an enhanced multi-scale attention fusion module(EMSA)is introdcued to replace the original feature fusion method based on upsampling concatenation and convolution.This module improves the efficiency of cross-scale information transmission in scenarios where small pothole features are diluted and large pothole features are confused with the background.It achieves this by combining dynamic attention calibration of pothole feature weights,grouped spatial refinement of pothole edges,and residual feature fusion.Experimental results show that the improved model achieves mAP@50 and mAP@0.50～0.95 scores of 86.6%and 58.3%respectively,representing improvements of 5.74%and 11.69%over the baseline YOLOv11n model.The recall rate reaches 82.7%,a 19.68%increase compared to the baseline.The experimental results demonstrate that the proposed optimization strategies effectively improve the model's detection ability for multi-scale potholes with weak features and reduce the miss rate in pothole detection tasks for highway images captured by UVAs.关键词
公路智能巡检/路面病害检测/深度学习/无人机视角Key words
intelligent inspection of highways/road damage detection/deep learning/UAV perspective分类
交通工程引用本文复制引用
李洪涛,王琳虹,刘晨浩,韦明..改进YOLOv11的无人机航拍公路坑槽检测算法[J].华南理工大学学报（自然科学版）,2026,54(6):100-109,10.基金项目
吉林省科技发展计划项目(20250203068SF)Supported by the Science and Technology Development Plan Project of Jilin Province(20250203068SF) （20250203068SF）
