Monitoring asset conditions is a crucial factor in building efficient
transportation asset management. Because of substantial advances in image
processing, traditional manual classification has been largely replaced by
semi-automatic/automatic techniques. As a result, automated asset detection and
classification techniques are required. This paper proposes a methodology to
detect and classify roadway pavement cracks using the well-known You Only Look
Once (YOLO) version five (YOLOv5) and version 8 (YOLOv8) algorithms.
Experimental results indicated that the precision of pavement crack detection
reaches up to 67.3% under different illumination conditions and image sizes.
The findings of this study can assist highway agencies in accurately detecting
and classifying asset conditions under different illumination conditions. This
will reduce the cost and time that are associated with manual inspection, which
can greatly reduce the cost of highway asset maintenance.

本研究提出了使用 You Only Look Once (YOLO) 版本五（YOLOv5）和版本八（YOLOv8）算法来检测和分类道路路面裂缝的方法。实验结果表明，在不同照明条件和图像尺寸下，路面裂缝的检测精度可达 67.3%。该研究的发现可以帮助公路管理机构准确地检测和分类不同照明条件下的资产状况，减少人工检查的成本和时间，极大地降低公路资产维护的成本。

基于深度学习的自动道路裂缝检测与分类

Automated Pavement Cracks Detection and Classification Using Deep  Learning

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 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 semantic information to low-level features for
crack detection in a feature pyramid way. And, it balances the contribution of
both easy and hard samples to loss by nested sample reweighting in a
hierarchical way. To demonstrate the superiority and generality of the proposed
method, we evaluate the proposed method on five crack datasets and compare it
with state-of-the-art crack detection, edge detection, semantic segmentation
methods. Extensive experiments show that the proposed method outperforms these
state-of-the-art methods in terms of accuracy and generality.

为了确保道路安全，需要进行路面裂缝检测。本研究提出一种基于深度学习的新型网络体系结构，称为特征金字塔和分层增强网络（FPHBN），以解决路面裂缝检测中的难题。实验表明，该方法在准确性和通用性方面优于现有的检测、边缘检测和语义分割方法。