DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection

• URL: http://arxiv.org/abs/2409.01604v1
• Date: Tue, 3 Sep 2024 04:53:32 GMT
• Title: DAPONet: A Dual Attention and Partially Overparameterized Network for Real-Time Road Damage Detection
• Authors: Weichao Pan, Jiaju Kang, Xu Wang, Zhihao Chen, Yiyuan Ge,
• Abstract summary: We propose DAPONet to enhance real-time road damage detection using street view image data (SVRDD) 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 COCO 2017 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.
• Score: 4.185368042845483
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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