Current road damage detection methods, relying on manual inspections or sensor-mounted vehicles, are inefficient, limited in coverage, and often inaccurate, especially for minor damages, leading to delays and safety hazards. To address these issues and enhance real-time road damage detection using street view image data (SVRDD), we propose DAPONet, a model incorporating three key modules: a dual attention mechanism combining global and local attention, a multi-scale partial over-parameterization module, and an efficient downsampling module. DAPONet achieves a mAP50 of 70.1% on the SVRDD dataset, outperforming YOLOv10n by 10.4%, while reducing parameters to 1.6M and FLOPs to 1.7G, representing reductions of 41% and 80%, respectively. On the MS COCO2017 val dataset, DAPONet achieves an mAP50-95 of 33.4%, 0.8% higher than EfficientDet-D1, with a 74% reduction in both parameters and FLOPs.

本研究针对现有道路损坏检测方法效率低、覆盖范围有限及准确性差的问题，提出了DAPONet模型。该模型采用了双重注意力机制、多尺度部分过参数化模块及高效下采样模块，实现了在SVRDD数据集上70.1%的mAP50，较YOLOv10n提升10.4%。DAPONet显著减少了参数和FLOPs，展现了其在实时道路损坏检测中的潜在影响。

双重注意力和部分过参数化网络用于实时道路损坏检测