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<p>Accurate recognition of cracks in asphalt pavements is fundamental to proactive maintenance and infrastructure safety management. From the repairing perspective, the identification process of the ruined surface areas on the pavement is more about finding the cracks and their location without requiring precise curve estimation. If we treat the problem as a binary semantic segmentation, we can translate this statement as the recall capability is higher ranked than the given model’s precision, where one of the main challenges is the varying crack widths. In this paper, we focus on modifying a standard U-Net network with the voting-based segmentation head called HoughNet, created for object detection and segmentation tasks, and with an additional reconstruction head for pixel-level information preservation. While combining them with the most appropriate loss functions, we measured performance on two datasets with slightly different crack characteristics. We showed that the model trained and evaluated on the Crack500 dataset can find more relevant cracked surface parts with a similar magnitude of precision compared to the competitor architectures. To see the limitations of the concept, we also report results on GAPS384 with thinner crack lines, where the concept can shift the model performance towards finding the best F1 Scores at both the dataset and image scale.</p>

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Enhancing recall in asphalt pavement crack segmentation using a HoughNet-extended U-Net architecture with reconstruction head

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月18日

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  • István Reményi,
  • Ámon Kiss,
  • Zoltán Kárász,
  • János Botzheim

摘要

Accurate recognition of cracks in asphalt pavements is fundamental to proactive maintenance and infrastructure safety management. From the repairing perspective, the identification process of the ruined surface areas on the pavement is more about finding the cracks and their location without requiring precise curve estimation. If we treat the problem as a binary semantic segmentation, we can translate this statement as the recall capability is higher ranked than the given model’s precision, where one of the main challenges is the varying crack widths. In this paper, we focus on modifying a standard U-Net network with the voting-based segmentation head called HoughNet, created for object detection and segmentation tasks, and with an additional reconstruction head for pixel-level information preservation. While combining them with the most appropriate loss functions, we measured performance on two datasets with slightly different crack characteristics. We showed that the model trained and evaluated on the Crack500 dataset can find more relevant cracked surface parts with a similar magnitude of precision compared to the competitor architectures. To see the limitations of the concept, we also report results on GAPS384 with thinner crack lines, where the concept can shift the model performance towards finding the best F1 Scores at both the dataset and image scale.