  Automated pavement distresses detection using road images remains a
challenging topic in the computer vision research community. Recent
developments in deep learning has led to considerable research activity
directed towards improving the efficacy of automated pavement distress
identification and rating. Deep learning models require a large ground truth
data set, which is often not readily available in the case of pavements. In
this study, a labeled dataset approach is introduced as a first step towards a
more robust, easy-to-deploy pavement condition assessment system. The technique
is termed herein as the Pavement Image Dataset (PID) method. The dataset
consists of images captured from two camera views of an identical pavement
segment, i.e., a wide-view and a top-down view. The wide-view images were used
to classify the distresses and to train the deep learning frameworks, while the
top-down view images allowed calculation of distress density, which will be
used in future studies aimed at automated pavement rating. For the wide view
group dataset, 7,237 images were manually annotated and distresses classified
into nine categories. Images were extracted using the Google Application
Programming Interface (API), selecting street-view images using a python-based
code developed for this project. The new dataset was evaluated using two
mainstream deep learning frameworks: You Only Look Once (YOLO v2) and Faster
Region Convolution Neural Network (Faster R-CNN). Accuracy scores using the F1
index were found to be 0.84 for YOLOv2 and 0.65 for the Faster R-CNN model
runs; both quite acceptable considering the convenience of utilizing Google
maps images.
