donstuAdvanced Engineering Research (Rostov-on-Don)Advanced Engineering Research (Rostov-on-Don)2687-1653Don State Technical University10.23947/1992-5980-2019-19-1-63-73donstu-1470Research ArticleINFORMATION TECHNOLOGY, COMPUTER SCIENCE AND MANAGEMENTИНФОРМАТИКА, ВЫЧИСЛИТЕЛЬНАЯ ТЕХНИКА И УПРАВЛЕНИЕDeep convolution neural network model in problem of crack segmentation on asphalt imagesМодель глубокой сверточной нейронной сети в задаче сегментации трещин на изображениях асфальтаhttps://orcid.org/0000-0003-2920-6478СобольБ. В.SobolB. V.

Соболь Борис Владимирович, заведующий кафедрой «Информационные технологии»,  доктор технических наук, профессор,

344000, г. Ростов-на-Дону, пл. Гагарина, 1

Sobol, Boris V., Head of the Information Technologies Department, Dr.Sci. (Eng.), professor 

1, Gagarin sq., Rostov-on-Don, 344000

b.sobol@mail.ruhttps://orcid.org/0000-0001-8465-5554СоловьевА. Н.SolovievA. N.

Соловьев Аркадий Николаевич, заведующий кафедры «Теоретическая и прикладная механика», доктор физикоматематических наук, профессор,

344000, г. Ростов-наДону, пл. Гагарина, 1

Soloviev, Arkady N., Head of the Theoretical and Applied Mechanics Department, Dr.Sci. (Phys.-Math.), professor 

1, Gagarin sq., Rostov-on-Don, 344000

solovievarc@gmail.comhttps://orcid.org/0000-0003-4112-7449ВасильевП. В.VasilievP. V.

Васильев Павел Владимирович, старший преподаватель кафедры «Информационные технологии»

344000, г. Ростов-на-Дону, пл. Гагарина, 1

Vasiliev, Pavel V., Senior lecturer of the Information Technologies Department,

1, Gagarin sq., Rostov-on-Don, 344000

lyftzeigen@mail.ruhttps://orcid.org/0000-0001-9476-5802ПодколзинаЛ. А.PodkolzinaL. A.

Подколзина Любовь Александровна, Аспирант 2-го года обучения кафедры «Информационные технологии»

344000, г. Ростов-на-Дону, пл. Гагарина, 1

Podkolzina, Lubov A., Post-graduate student of the Information Technologies Department, 

1, Gagarin sq., Rostov-on-Don, 344000

podkolzinalu@gmail.comДонской государственный технический университет, г. Ростов-на-ДонуРоссияDon State Technical University, Rostov-on-DonRussian Federation2019010420191916373Copyright © Sobol B.V., Soloviev A.N., Vasiliev P.V., Podkolzina L.A., 20192019Соболь Б.В., Соловьев А.Н., Васильев П.В., Подколзина Л.А.Sobol B.V., Soloviev A.N., Vasiliev P.V., Podkolzina L.A.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vestnik-donstu.ru/jour/article/view/1470Introduction

Introduction. Early defect illumination (cracks, chips, etc.) in the high traffic load sections enables to reduce the risk under emergency conditions. Various photographic and video monitoring techniques are used in the pavement managing system. Manual evaluation and analysis of the data obtained may take unacceptably long time. Thus, it is necessary to improve the conditional assessment schemes of the monitor objects through the autovision.

Materials and Methods

Materials and Methods. The authors have proposed a model of a deep convolution neural network for identifying defects on the road pavement images. The model is implemented as an optimized version of the most popular, at this time, fully convolution neural networks (FCNN). The teaching selection design and a two-stage network learning process considering the specifics of the problem being solved are shown. Keras and TensorFlow frameworks were used for the software implementation of the proposed architecture.

Research Results

Research Results. The application of the proposed architecture is effective even under the conditions of a limited amount of the source data. Fine precision is observed. The model can be used in various segmentation tasks. According to the metrics, FCNN shows the following defect identification results: IoU - 0.3488, Dice - 0.7381.

Discussion and Conclusions. The results can be used in the monitoring, modeling and forecasting process of the road pavement wear.

Введение

Введение. Своевременное устранение дефектов (трещин,  сколов и пр.) на участках повышенной нагрузки дорожного полотна позволяет снизить риск возникновения аварийных ситуаций. В настоящее время для контроля состояния дорожного покрытия применяются различные методы фото- и видеонаблюдения. Оценка и анализ полученных данных в ручном режиме могут занять недопустимо много времени. Таким образом, необходимо совершенствовать процедуры осмотра и оценки состояния объектов контроля с помощью технического зрения.

Материалы и методы

Материалы и методы. Авторами предложена модель глубокой сверточной нейронной сети для идентификации дефектов на изображениях дорожного покрытия. Модель реализована как оптимизированный вариант наиболее популярных на данный момент полностью сверточных нейронных сетей (FCNN). Показано построение обучающей выборки и двухэтапный процесс обучения сети с учетом специфики решаемой задачи. Для программной реализации предложенной архитектуры использовались фреймворки Keras и TensorFlow.

Результаты исследования

Результаты исследования. Применение предложенной архитектуры эффективно даже в условиях ограниченного объема исходных данных. Отмечена высокая степень повторяемости результатов. Модель может быть использована в различных задачах сегментации. Согласно метрикам, FCNN показывает следующие результаты идентификации дефектов: IoU — 0,3488, Dice — 0,7381.

Обсуждение и заключения

Обсуждение и заключения. Полученные результаты могут быть использованы в процессе мониторинга, моделирования и прогнозирования процессов износа дорожных покрытий.

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