Lightweight bridge crack image detection algorithm based on improved YOLOv11n
SUN Wei
LIU Wenjiang
Abstract：To address the problems of low accuracy in current bridge crack image detection and large algorithm scale that is inconvenient for deployment on resource-constrained edge devices,a lightweight bridge crack image detection algorithm based on improved YOLOv11n(you only look once version 11 nano)is proposed.By integrating the ShuffleNetV2 module with CCFM(cross-scale fusion module),a Shuffle-CCFM structure is constructed to enhance multi-scale feature fusion capability while reducing algorithm parameters.The iRMB(inverted residual mobile block with attention)is introduced into the C2PSA(cross-channel partial spatial attention)module to form the C2PSA-iRMB module,which improves the algorithm's recognition capability for complex crack details and enhances the correlation modeling capability of spatially distant features within the same crack structure region.WTConv(wavelet transform convolution)is integrated into the C3k2 module to form the C3k2-WTConv module,improving the model's feature extraction capability at different scales.DySample is adopted to replace the traditional upsampling module,adaptively adjusting sampling positions according to feature map content to enhance spatial resolution and detail restoration capability during the upsampling stage.Ablation experiments,comparative experiments,and visualization detection effect experiments are conducted to evaluate the detection performance of the improved YOLOv11n algorithm.The experimental results show that:compared with the YOLOv11n algorithm,after introducing the Shuffle-CCFM structure,C2PSA-iRMB module,C3k2-WTConv module,and DySample module,the improved YOLOv11n algorithm's params NP,computation cost Nf,and weight file size T are reduced by 27.5％,23.8％,and 32.7％,respectively,while mean average precision at intersection over union threshold of 50 EmAP50,mean average precision at intersection over union threshold from 50 to 95 EmAP50-95,and recall R increase by 1.6％,3.8％,0.4％respectively,demonstrating significant improvements in algorithm lightweighting and detection accuracy.The improved YOLOv11n algorithm's detection accuracy and performance indicators for bridge crack images are significantly superior to lightweight algorithms such as YOLOv5n,YOLOv6n,YOLOv8n,and YOLOv10n,making it suitable for deployment on edge devices with limited computational resources.The improved YOLOv11n algorithm demonstrates higher confidence in detection result precision in bridge crack visualization detection experiments,exhibits stronger capability in capturing details of minute-sized and morphologically complex cracks,and possesses stronger anti-interference capability in complex backgrounds.
Keywords：bridge crack image detectionYOLOv11nShuffleNetV2CCFMiRMBWTConvDySample
Publication Date：2025-11-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：11( 95-105 )