ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2022-22-3-459-471ntv-240Research ArticleОПТИЧЕСКИЕ СИСТЕМЫ И ТЕХНОЛОГИИOPTICAL ENGINEERINGОбнаружения выбоин на дорожных покрытиях с использованием методов фотограмметрии и дистанционного зондирования (обзор)Detection of potholes on road surfaces using photogrammetry and remote sensing methods (review)https://orcid.org/0000-0002-0498-0654Абд МуктиШ. Н.Abd MuktiSh. N.

Абд Мукти Шахрул Низан — PhD, старший геодезист

Куала-Лумпур, 50350

Shahrul N. Abd Mukti — PhD, Senior Surveyor

Kuala Lumpur, 50350

shahrulnizan58@gmail.comhttps://orcid.org/0000-0003-2065-7993ТахарХ. Н.TaharKh. N.

Тахар Хайрул Низам — PhD, доцент

Шах-Алам, 40450

sc 38362352000

Khairul N. Tahar — PhD, Associate Professor

Shah Alam, 40450

sc 38362352000

nizamtahar@gmail.comАдминистрация Куала-ЛумпураМалайзияDewan Bandaraya Kuala LumpurMalaysiaТехнологический университет MARA (UiTM)МалайзияUniversiti Teknologi MARA (UiTM)Malaysia202216122024223459471Copyright © Абд Мукти Ш.Н., Тахар Х.Н., 20242024Абд Мукти Ш.Н., Тахар Х.Н.Abd Mukti S.N., Tahar K.N.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/240

Приведен обзор методов получения двухмерных (2D) и трехмерных (3D) моделей дефектов на дорожном покрытии. На целостность дорожного покрытия могут влиять такие факторы, как температура, влажность, атмосферные воздействия и нагрузки. Один из самых распространенных видов разрушения дорожного покрытия выбоины, которые являются признаками структурных разрушений асфальтовой дороги. Процесс сбора и анализа данных имеет решающее значение при обслуживании дорожного покрытия. Обнаружение и количественная оценка информации о геометрии выбоин необходима для понимания прогноза работ по содержанию дорог и для определения правильных стратегий ухода за асфальтовым покрытием. Визуальное обнаружение дорожных дефектов дорогостоящее и трудоемкое. В настоящее время в научных работах представлены многочисленные исследования, показывающие способы автоматического обнаружения и распознавания выбоин. В настоящей работе рассмотрены методы автоматического обнаружения и классификации выбоин с использованием инструментальных средств — датчиков, интегрированных с системой позиционирования. Техника обработки 2D-изображений с использованием методов машинной классификации позволяет определить и уточнить геометрию выбоины. Для повышения точности обработки изображений и выделения краев выбоин применяются такие алгоритмические методы как искусственные нейронные сети, деревья решений, методы опорных векторов и нечеткой классификации. 3D-модель выбоины может быть получена на основе данных лазерного сканирования и методов фотограмметрии. В работе обобщены различные методы и предложенная техника для извлечения 3D-модели выбоины. Результаты работы могут найти применение для улучшения инфраструктуры обслуживания дорожных покрытий.

An overview of methods for obtaining 2D and 3D models of defects on the pavement is given. The integrity of the pavement can be affected by factors such as temperature, humidity, weathering and loads. Potholes are one of the most common types of pavement failure. These defects are the signs of structural failures in an asphalt road. The process of collecting and analyzing data is critical to pavement maintenance. Finding and quantifying pothole geometry information is essential to understand road maintenance forecasts and to determine the right asphalt maintenance strategies. Visual detection of road defects is costly and time consuming. Today, there are quite a lot of studies in the scientific literature showing methods for automatic detection and recognition of potholes. In our work, we consider methods for automatic detection and classification of potholes using tools — sensors integrated with a positioning system. The technique of processing two-dimensional (2D) images using various methods of machine classification allows you to determine the precise geometry of the pothole. Algorithmic methods such as artificial neural networks, decision trees, support vector machines, and fuzzy classification are used to improve the accuracy of image processing and highlight the edges of potholes. A three-dimensional model of the pothole (3D) can be obtained based on laser scanning data and photogrammetry methods. The paper summarizes various methods and proposed techniques for extracting a 3D pothole model. The results of the work can be used to improve the infrastructure for maintaining road surfaces.

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The authors declare that there are no conflicts of interest present.