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An improved YOLOv8n network model is proposed to cope with key challenges in road damage detection, including feature extraction, multi-scale feature processing, fusion, and efficiency. By integrating the feature extraction structure RepVGG-SSE and the multi-branch downsampling into the backbone, the receptive field of our model is broadened so that it is capable of dealing with the diverse road damage scales. As part of our model, the Efficient-GFPN feature pyramid structure makes effective fusion of multi-scale features possible, and the performance for detecting objects of different sizes and complexities is enhanced greatly. Additionally, the lightweight convolution model GPConv is proposed to replace the 3x3 Conv in the C2f structure in the neck layer, so that both the parameters and computational complexity of the network model can be reduced greatly without compromising accuracy, so as to achieve the balance of efficiency and performance of the detection model in a reasonable way. The Improved YOLOv8n network was trained and validated on the RDD-2020 and UAPD datasets, and both the ablation and comparison experimental results demonstrate that the improved YOLOv8n model is both effective and efficient, and outperforms the state-of-the-art methods, suggesting it a promising solution to the real-world road damage detection tasks.
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