To accurately detect road damages with large size differences and small scales in vehicle-mounted images,this paper presents a real-time road damage detection model based on improved YOLOv5s,termed as VRD-YOLO (Vehicle-mounted image Road Damage Detection-YOLO).Firstly,a Channel Mix Slide Transformer (CMST) module is proposed to enhance the model's global context modeling capability and strengthen the extraction of fine-grained road damage semantic feature information.Secondly,a generalized feature pyramid with cross-layer fusion and cross-scale fusion is introduced to expand the network receptive field and strengthen the fusion of multi-scale damage features.Thirdly,to optimize the model's feature response and further improve detection performance,a dynamic detection head is designed to achieve scale perception,spatial perception,and task perception.Finally,a Vehicle-mounted Image Road Damage Dataset (VIRDD) is constructed to expand the quantity and types of existing road damage datasets,and ablation and comparative experiments are conducted based on this dataset.Experimental results show that the VRD-YOLO achieves a mean Average Precision (mAP@0.5) of 74.45% on the VIRDD dataset,with a detection speed reaching 28.56 frames per second.Compared to the YOLOv5s model,VRD-YOLO improves the precision,recall,F1 score,and mAP by 2.79,2.32,2.54,and 3.19 percentage points,respectively.Additionally,compared with six other classical and mainstream object detection models,the proposed VRD-YOLO attains the highest detection accuracy with the smallest model parameter count of 9.68 million,verifying its effectiveness and superiority.