In this study, a supervised machine learning network model is proposed to detect and classify various types of cracks developed in asphalt pavements, including lane markers. Crack images captured from a digital camera are classified into nine categories following the pavement distress identification manual proposed by the Federal Highways Administration (FHWA). These categories are three different types of cracks, such as fatigue, longitudinal, and transverse cracks with three severity levels of the low, medium, and high for each crack type. To establish a training dataset for crack detection, 1000 images with the original size of 3704 × 10,000 pixels are divided into 20,000 smaller images of 1852 × 1000 pixels image size. The training images are labeled based on the nine categories and trained using an updated version of faster R-CNN called RetinaNet. The trained network model is validated using pavement surface images obtained from 2400 m of two road sections. It is observed from the validation study that the detection and classification accuracy of the trained network model is 84.9% considering both the crack type and severity level. When considering the crack type only, the detection accuracy of the network model is 89.1%.

在这项研究中，提出了一种有监督的机器学习网络模型来检测和分类在沥青路面上形成的各种类型的裂缝，包括车道标志。根据联邦公路管理局（FHWA）提出的路面遇险识别手册，从数码相机捕获的裂缝图像分为九类。这些类别是三种不同类型的裂纹，例如疲劳，纵向和横向裂纹，每种裂纹类型的严重性级别分别为低，中和高。为了建立用于裂纹检测的训练数据集，将原始大小为3704×10,000像素的1000张图像分为185××1000像素图像大小的20,000张较小图像。训练图像根据9个类别进行标记，并使用称为RetinaNet的更快R-CNN的更新版本进行训练。使用从2400 m的两个路段获得的路面图像来验证训练后的网络模型。从验证研究中可以看出，考虑到裂纹类型和严重程度，训练后的网络模型的检测和分类精度为84.9％。仅考虑裂纹类型时，网络模型的检测精度为89.1％。