Pavement crack detection is a critical task for insuring road safety. Manual crack detection is extremely time-consuming. Therefore, an automatic road crack detection method is required to boost this progress. However, it remains a challenging task due to the intensity inhomogeneity of cracks and complexity of the background, e.g., the low contrast with surrounding pavements and possible shadows with a similar intensity. Inspired by recent advances of deep learning in computer vision, we propose a novel network architecture, named feature pyramid and hierarchical boosting network (FPHBN), for pavement crack detection. The proposed network integrates context information to low-level features for crack detection in a feature pyramid way, and it balances the contributions of both easy and hard samples to loss by nested sample reweighting in a hierarchical way during training. In addition, we propose a novel measurement for crack detection named average intersection over union (AIU). To demonstrate the superiority and generalizability of the proposed method, we evaluate it on five crack datasets and compare it with the state-of-the-art crack detection, edge detection, and semantic segmentation methods. The extensive experiments show that the proposed method outperforms these methods in terms of accuracy and generalizability. Code and data can be found in https://github.com/fyangneil/pavement-crack-detection.

中文翻译：

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用于路面裂缝检测的特征金字塔和分层提升网络

路面裂缝检测是确保道路安全的关键任务。手动裂纹检测非常耗时。因此，需要一种自动道路裂缝检测方法来推动这一进展。然而，由于裂缝强度的不均匀性和背景的复杂性，例如与周围路面的低对比度和具有相似强度的可能阴影，这仍然是一项具有挑战性的任务。受计算机视觉深度学习最新进展的启发，我们提出了一种新的网络架构，名为特征金字塔和分层提升网络（FPHBN），用于路面裂缝检测。所提出的网络以特征金字塔的方式将上下文信息集成到低级特征中以进行裂缝检测，它通过在训练期间以分层方式嵌套样本重新加权来平衡简单样本和困难样本对损失的贡献。此外，我们提出了一种新的裂缝检测测量方法，称为平均交叉并集（AIU）。为了证明所提出方法的优越性和通用性，我们在五个裂纹数据集上对其进行了评估，并将其与最先进的裂纹检测、边缘检测和语义分割方法进行了比较。大量实验表明，所提出的方法在准确性和泛化性方面优于这些方法。代码和数据可以在 https://github.com/fyangneil/pavement-crack-detection 中找到。为了证明所提出方法的优越性和通用性，我们在五个裂纹数据集上对其进行了评估，并将其与最先进的裂纹检测、边缘检测和语义分割方法进行了比较。大量实验表明，所提出的方法在准确性和泛化性方面优于这些方法。代码和数据可以在 https://github.com/fyangneil/pavement-crack-detection 中找到。为了证明所提出方法的优越性和通用性，我们在五个裂纹数据集上对其进行了评估，并将其与最先进的裂纹检测、边缘检测和语义分割方法进行了比较。大量实验表明，所提出的方法在准确性和泛化性方面优于这些方法。代码和数据可以在 https://github.com/fyangneil/pavement-crack-detection 中找到。