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<p>Intelligent detection of pavement cracks is crucial for infrastructure maintenance, where timely detection of cracks and damage significantly prolongs pavement service life. However, current detection models still have limitations in terms of accuracy. To address this shortcoming, this study proposes a pavement crack real-time detection transformer (PC-DETR) for accurate real-time pavement crack detection. PC-DETR, based on the real-time detection transformer (RT-DETR), integrates the SENetV2 feature fusion mechanism, semantic and detail infusion mechanism, and inner-Focaler-weighted intersection-over-union(IFWIoU) loss function. PC-DETR, the first method to use the RT-DETR model in the field of pavement crack detection, determines the proposed IFWIoU loss function, which combines weighted intersection-over-union (IoU), Focaler-IoU, and Inner-IoU functions. This integration strengthens global feature extraction, reduces information loss in minor defects, and enhances the synergy between the deep and shallow feature layers. Our experimental validation on the RDD2022 dataset demonstrated the superior performance of the proposed model: PC-DETR achieved 55.3 GFLOPs. A road surface inspection vehicle using this method was found to effectively detect pavement cracks in real scenarios at a speed of 40 frames per second.</p>

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A real-time multiscale pavement crack-detection model

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • Xingju Wang,
  • Yiqian Liang,
  • Xingyu Yang,
  • Mengqi Guo,
  • Tongyao Jia,
  • Muhammad Kamran

摘要

Intelligent detection of pavement cracks is crucial for infrastructure maintenance, where timely detection of cracks and damage significantly prolongs pavement service life. However, current detection models still have limitations in terms of accuracy. To address this shortcoming, this study proposes a pavement crack real-time detection transformer (PC-DETR) for accurate real-time pavement crack detection. PC-DETR, based on the real-time detection transformer (RT-DETR), integrates the SENetV2 feature fusion mechanism, semantic and detail infusion mechanism, and inner-Focaler-weighted intersection-over-union(IFWIoU) loss function. PC-DETR, the first method to use the RT-DETR model in the field of pavement crack detection, determines the proposed IFWIoU loss function, which combines weighted intersection-over-union (IoU), Focaler-IoU, and Inner-IoU functions. This integration strengthens global feature extraction, reduces information loss in minor defects, and enhances the synergy between the deep and shallow feature layers. Our experimental validation on the RDD2022 dataset demonstrated the superior performance of the proposed model: PC-DETR achieved 55.3 GFLOPs. A road surface inspection vehicle using this method was found to effectively detect pavement cracks in real scenarios at a speed of 40 frames per second.