Pavement distresses lead to pavement deterioration and failure. Accurate identification and classification of distresses helps agencies evaluate the condition of their pavement infrastructure and assists in decision-making processes on pavement maintenance and rehabilitation. The state of the art is automated pavement distress detection using vision-based methods. This study implements two deep learning techniques, Faster Region-based Convolutional Neural Networks (R-CNN) and You Only Look Once (YOLO) v3, for automated distress detection and classification of high resolution (1,800 × 1,200) three-dimensional (3D) asphalt and concrete pavement images. The training and validation dataset contained 625 images that included distresses manually annotated with bounding boxes representing the location and types of distresses and 798 no-distress images. Data augmentation was performed to enable more balanced representation of class labels and prevent overfitting. YOLO and Faster R-CNN achieved 89.8% and 89.6% accuracy respectively. Precision-recall curves were used to determine the average precision (AP), which is the area under the precision-recall curve. The AP values for YOLO and Faster R-CNN were 90.2% and 89.2% respectively, indicating strong performance for both models. Receiver operating characteristic (ROC) curves were also developed to determine the area under the curve, and the resulting area under the curve values of 0.96 for YOLO and 0.95 for Faster R-CNN also indicate robust performance. Finally, the models were evaluated by developing confusion matrices comparing our proposed model with manual quality assurance and quality control (QA/QC) results performed on automated pavement data. A very high level of match to manual QA/QC, namely 97.6% for YOLO and 96.9% for Faster R-CNN, suggest the proposed methodology has potential as a replacement for manual QA/QC.

路面损坏会导致路面变质和损坏。准确识别和分类故障，有助于机构评估路面基础设施的状况，并协助进行路面维护和修复的决策过程。现有技术是使用基于视觉的方法进行的自动路面遇险检测。这项研究实施了两种深度学习技术，即基于快速区域的卷积神经网络（R-CNN）和仅查看一次（YOLO）v3，用于自动遇险检测和高分辨率（1,800×1,200）三维（3D）分类沥青和混凝土路面图像。训练和验证数据集包含625张图像，其中包括手动表示的遇险，并用边界框表示遇险的位置和类型以及798幅未遇险图像。进行数据扩充是为了使类标签的显示更加均衡，并防止过度拟合。YOLO和Faster R-CNN分别达到89.8％和89.6％的准确性。使用精确度调用曲线确定平均精确度（AP），即平均精确度曲线下的面积。YOLO和Faster R-CNN的AP值分别为90.2％和89.2％，表明这两个模型的性能都很强。还开发了接收器工作特性（ROC）曲线来确定曲线下的面积，对于YOLO，曲线下的最终面积值为0.96，对于Faster R-CNN，曲线下的最终面积值为0.95，也表明性能稳定。最后，通过开发混淆矩阵对模型进行评估，将我们提出的模型与在自动路面数据上执行的手动质量保证和质量控制（QA / QC）结果进行比较。与手动QA / QC的匹配程度非常高，即YOLO为97.6％，Faster R-CNN为96.9％，表明所提出的方法有可能替代手动QA / QC。