华南理工大学学报（自然科学版）2026，Vol.54Issue(5)：47-58,12.DOI:10.12141/j.issn.1000-565X.250158
基于改进YOLOv11的轻量化裂缝检测算法
A Lightweight Crack Detection Algorithm Based on Improved YOLOv11
摘要
Abstract
Road pavements,as critical transportation infrastructure,are prone to cracks and other defects under long-term load bearing and environmental erosion.Traditional manual inspection methods suffer from low effi-ciency,high subjectivity,and significant safety risks,making it difficult to meet the maintenance demands of large-scale pavement networks.This paper proposes a lightweight,high-precision pavement crack detection algorithm named LMC-YOLO(Lightweight MobileNetV4 with CAA for YOLO)that addresses feature loss and insufficient ac-curacy in detecting thin and elongated cracks.The algorithm systematically optimizes the backbone structure,atten-tion mechanisms,and lightweighting strategies of the detection network.By introducing the lightweight Mobile-NetV4(MNV4)structure into the backbone network and utilizing its efficient Universal Inverted Bottleneck(UIB)modules,the algorithm achieves a balance between powerful feature extraction capability and low computational cost.An improved Context Anchor Attention(CAA)mechanism is integrated into the neck of the detection network,along with a crack shape-aware module based on strip convolution,effectively enhancing the detection capability for elongated cracks.Through refined network design,the number of model parameters is reduced by 23.3%,computa-tional complexity is decreased to 4.6 GFLOPs.Experimental results demonstrate that LMC-YOLO achieves 91.1%precision,84.6%mAP@0.5,and 70.3%mAP@0.5∶0.95 on crack detection tasks,with an F1-score of 80.40%and an inference speed of 345 frames per second.Cross-dataset validation on DIOR and DOTA-v2 further confirms the model's cross-domain transfer capability.This method successfully achieves an effective combination of high accuracy and efficient lightweight design,providing a practical solution for real-time pavement crack detection on mobile and embedded devices.关键词
道路工程/裂缝检测/轻量级目标检测/注意力机制/深度学习Key words
road engineering/crack detection/lightweight object detection/attention mechanism/deep learning分类
交通工程引用本文复制引用
纪泳丞,李毅,陈汉平,梁洋..基于改进YOLOv11的轻量化裂缝检测算法[J].华南理工大学学报（自然科学版）,2026,54(5):47-58,12.基金项目
黑龙江省优秀青年科学基金项目(YQ2024E004) （YQ2024E004）
黑龙江省交通运输厅科技项目(HJK2023B009)Supported by the Excellent Youth Science Foundation of Heilongjiang Province(YQ2024E004)and the Sci-ence and Technology Project of Heilongjiang Provincial Department of Transportation(HJK2023B009) （HJK2023B009）
