Technical vision for monitoring and diagnostics of the road surface quality in the smart city program

Abstract

This article is devoted to the research and development of methods for the automated detection of road surface defects in offline mode. The article discusses the problems encountered in the operation of an automated road scanner (ARS), as well as the modernization of the system to solve these problems using computer (machine) vision and a Field-Programmable Gate Array (FPGA). The work uses deep learning methods and analysis of various architectures of neural networks. About 100 terabytes were collected and tagged to train the neural network for recognizing road defects. It is worth noting that the task of recognizing defects in the roadway is one of the most difficult even for the human eye, since the contours merge with the defect. During the study, a board was developed to collect telemetric data from road scanner devices. To store the collected telemetry characteristics, a large data storage was developed with replication and synchronization functions.