E3S Web Conf.
Volume 164, 2020Topical Problems of Green Architecture, Civil and Environmental Engineering 2019 (TPACEE 2019)
|Number of page(s)||6|
|Section||Energy Efficiency in Transportation|
|Published online||05 May 2020|
Technical vision for monitoring and diagnostics of the road surface quality in the smart city program
1 Moscow Technical University of Communication and Informatics, 8a Aviamotornaya street, Moscow, Russia
2 Moscow Automobile and Road Construction State Technical University, 64 Leningradsky prospect, Moscow, Russia
* Corresponding author: email@example.com
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.
© The Authors, published by EDP Sciences 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.