DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866912013067747328 |
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| author | Pan, Weichao Kang, Jiaju Wang, Xu Chen, Zhihao Ge, Yiyuan |
| author_facet | Pan, Weichao Kang, Jiaju Wang, Xu Chen, Zhihao Ge, Yiyuan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01604 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection Pan, Weichao Kang, Jiaju Wang, Xu Chen, Zhihao Ge, Yiyuan Computer Vision and Pattern Recognition 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. |
| title | DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.01604 |