Aiming to address the issues of low accuracy and missed detection of small-size targets in UAV road damage detection tasks,this paper proposes a road damage detection model for UAV images.The model is based on YOLOv8 and incorporates a data augmentation strategy to tackle the uneven distribution of road damage samples and enhance the effectiveness of model training.It integrates omni-dimensional dynamic convolution into the backbone network to improve feature extraction for road damage.Additionally,the feature fusion network employs a"gather-and-distribute"mechanism to better combine different feature maps.To reduce computational complexity,the model utilizes the ADown downsampling module.Finally,the Powerful-IoU loss function is employed to guide bounding box regression,speed up model convergence,and enhance localization accuracy of road damage targets.Testing on real datasets demonstrates that the improved model achieves increases in mean average precision of 11％,14.1％,8％,5.3％,1.6％,3.8％,and 4.2％,respectively,compared to seven other classical target detection models,confirming its effectiveness in UAV-based road damage detection.
维普
万方数据