改进YOLOv10n的轻量化道路裂缝检测模型OA

Aiming at the problem that the existing road crack detection model cannot effectively balance the detec-tion accuracy,computational complexity and detection speed with poor practical application effect,a lightweight road crack detection model YOLO-CGVE based on improved YOLOv10n was proposed.Firstly,the coordinate at-tention(CA)module was used to replace the partial self-attention(PSA)module to better capture the local and global relationships in space and improve the capacity to extract features.Secondly,the computational complexity was reduced by using lightweight GSConv to replace some standard convolutions in the backbone and neck net-works.Thirdly,the original C2f structure in the neck network was replaced by VoV-GSCSP,which allowed for the efficient merging of feature maps from various stages and further minimized computing complexity while maintaining accuracy.Finally,the ECIoU loss function was used to replace the original loss function to improve the detection box positioning accuracy and convergence speed.The experimental results on RDD2022_China dataset showed that compared with YOLOv10n,while keeping a high detection speed,the mAP@0.5 of YOLO-CGVE was improved by 2.4 percentage points,reaching 75.9%,and the number of parameters and the amount of computation were de-creased by 11.1%and 9.8%,respectively.YOLO-CGVE could better meet the application needs in environments with limited computing resources.