基于CA_SD_YOLO的高速公路路基病害检测方法研究OA

The accuracy and efficiency of highway subgrade defect detection are of vital importance for road maintenance.However,the existing detection networks are often limited to convolutional structures,and there is still room for improvement in multi-scale feature extraction and cross-scale spatial feature fusion.This paper proposes a CA_SD_YOLO network model to address these issues.This model introduces a convolutional attention fusion module(CAFM)in the backbone network.It extracts features through two branches and fuses them.The convolutional branch extracts local details,while the attention branch models global correlations,achieving complementary extraction of local convolutional features and global attention features.In the feature fusion layer,a spatial perturbation-aware module(SDPM)is proposed to replace the original concat module,using high-level features to weight low-level features,thereby achieving pixel-level cross-scale alignment and resolving misalignment between cross-scale semantic and structural information.The experimental results show that compared with YOLOv13,on the GPR_Data dataset,the overall mAP50,recall rate,and accuracy have increased by 5.6%,5.6%,and 2.9%,respectively.This model effectively enhances detection capability in complex scenarios,providing high-precision,low-latency automated defect-detection tools for road maintenance departments and a feasible solution for automatic identification of GPR road defects.