Rapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing (BG) algorithm, specifically designed for crack length estimation in asphalt pavements, along with an optimized OrthoBoundary algorithm tailored for crack width estimation. Leveraging four widely adopted deep learning models for asphalt pavement crack segmentation, four distinct sets of image segmentation results have been produced. Subsequently, a comprehensive evaluation has been conducted to assess the effectiveness of both crack dimensions estimation algorithms. The findings demonstrate that the integration of the BG algorithm, the optimized OrthoBoundary algorithm, and the fully convolutional network with the HRNet backbone achieve a prediction accuracy of 80.21% for crack length estimation and 84.32% for average width estimation. Moreover, the image processing speed, at a resolution of 3024 × 3024, can be maintained at approximately 5 s, with average width estimation observed to be up to 9.1-fold faster than the unoptimized OrthoBoundary algorithm. These results signify advancements in automated crack quantification methodologies, with implications for enhancing civil infrastructure maintenance practices.

中文翻译：

---

自动量化沥青路面的裂缝长度和宽度


快速、准确和全自动地估计沥青路面裂缝的长度和宽度对于实现主动资产管理至关重要，这带来了重大挑战，这主要是由于自动图像分割的有效性以及裂缝宽度和长度估计算法的准确性方面的限制。为了应对这一挑战，本文介绍了专为沥青路面裂缝长度估计而设计的分支增长 （BG） 算法，以及为裂缝宽度估计量身定制的优化 OrthoBoundary 算法。利用四种广泛采用的深度学习模型进行沥青路面裂缝分割，生成了四组不同的图像分割结果。随后，进行了综合评估以评估两种裂纹尺寸估计算法的有效性。结果表明，BG 算法、优化的 OrthoBoundary 算法以及全卷积网络与 HRNet 主干的集成实现了 80.21% 的裂纹长度估计预测精度和 84.32% 的平均宽度估计精度。此外，分辨率为 3024 × 3024 的图像处理速度可以保持在大约 5 秒，观察到的平均宽度估计比未优化的 OrthoBoundary 算法快 9.1 倍。这些结果标志着自动裂缝量化方法的进步，对加强土木基础设施维护实践具有重要意义。