A Depth Camera-Based Intelligent Method for Identifying and Quantifying Pavement Diseases

In this study, a depth camera-based intelligence method is proposed. First, road damage images are collected and transformed into a training set. Then training, defect detection, defect extraction, and classification are performed. In addition, a YOLOv5 is used to create, train, validate, and test the label database. The method does not require a predetermined distance between the measurement target and the sensor; can be applied to moving scenes; and is important for the detection, classification, and quantification of pavement diseases. The results show that the sensor can achieve plane fitting at investigated working distances by means of a deep learning network. In addition, two pavement examples show that the detection method can save a lot of manpower and improve the detection efficiency with certain accuracy.

This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met.