Модель глубокой сверточной нейронной сети в задаче сегментации трещин на изображениях асфальта

Модель глубокой сверточной нейронной сети в задаче сегментации трещин на изображениях асфальта

Автор: Соболь Б.В., Соловьев А.Н., Васильев П.В., Подколзина Л.А.

Журнал: Вестник Донского государственного технического университета @vestnik-donstu

Рубрика: Информатика, вычислительная техника и управление

Статья в выпуске: 1 т.19, 2019 года.

Бесплатный доступ

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. 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...

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Искусственные нейронные сети, идентификация дефектов, сегментация, дорожное покрытие, трещины

Короткий адрес: https://sciup.org/142219829

IDR: 142219829   |   DOI: 10.23947/1992-5980-2019-19-1-63-73

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