DAPONet：一种用于实时道路损伤检测的双注意力与部分过参数化网络
DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection
AI总结：
本文提出DAPONet，通过双注意力、多尺度部分过参数化和高效下采样模块，在实时道路损伤检测中以更少参数和计算量超越YOLOv10n与EfficientDet-D1。
摘要：
当前的道路损伤检测方法依赖人工巡检或传感器搭载车辆，效率低下、覆盖范围有限，且常常不够准确，尤其是对于轻微损伤，导致检测延迟和安全隐患。为了解决这些问题并利用街景图像数据增强实时道路损伤检测（SVRDD），我们提出了DAPONet，该模型包含三个关键模块：结合全局与局部注意力的双注意力机制、多尺度部分过参数化模块，以及高效下采样模块。DAPONet在SVRDD数据集上取得了70.1%的mAP50，比YOLOv10n高出10.4%，同时将参数量降至1.6M，FLOPs降至1.7G，分别减少了41%和80%。在MS COCO2017验证数据集上，DAPONet取得了33.4%的mAP50-95，比EfficientDet-D1高0.8%，参数量和FLOPs均减少74%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
