DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection
<|reference_start|>DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection: 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.<|reference_end|>
@article{pan2024daponet:,
    title={DAPONet: A Dual Attention and Partially Overparameterized Network for
  Real-Time Road Damage Detection},
    author={Weichao Pan, Jiaju Kang, Xu Wang, Zhihao Chen, Yiyuan Ge},
    journal={arXiv preprint arXiv:2409.01604},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.01604},
    primaryClass={cs.CV}
}