Deep Learning to Detect Road Distress from Unmanned Aerial System Imagery

Abstract

Surface distress is an indication of poor or unfavorable pavement performance or signs of impending failure that can be classified into a fracture, distortion, or disintegration. To mitigate the risk of failing roadways, effective methods to detect road distress are needed. Recent studies associated with the detection of road distress using object detection algorithms are encouraging. Although current methodologies are favorable, some of them seem to be inefficient, time-consuming, and costly. For these reasons, the present study presents a methodology based on the mask regions with convolutional neural network model, which is coupled with the new object detection framework Detectron2 to train the model that utilizes roadway imagery acquired from an unmanned aerial system (UAS). For a comprehensive understanding of the performance of the proposed model, different settings are tested in the study. First, the deep learning models are trained based on both high- and low-resolution datasets. Second, three different backbone models are explored. Finally, a set of threshold values are tested. The corresponding experimental results suggest that the proposed methodology and UAS imagery can be used as efficient tools to detect road distress with an average precision score up to 95%.