ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2022-22-1-33-46ntv-256Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEДетектирование состояния зевоты у водителя транспортного средства при помощи модели сверточной нейронной сетиDetection of yawning in driver behavior based on a convolutional neural networkhttps://orcid.org/0000-0001-6418-4660ЛашковИ. Б.LashkovI. B.

Лашков Игорь Борисович — кандидат технических наук, старший научный сотрудник

sc 56719631000

Санкт-Петербург, 199178

Igor B. Lashkov — PhD, Senior Researcher

sc 56719631000

Saint Petersburg, 199178

igor-lashkov@ya.ruСанкт-Петербургский Федеральный исследовательский центр Российской академии наук (СПб ФИЦ РАН)РоссияSt. Petersburg Federal Research Center of the Russian Academy of SciencesRussian Federation2022161220242213346Copyright © Лашков И.Б., 20242024Лашков И.Б.Lashkov I.B.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/256

Предмет исследования. Среди факторов, потенциально являющихся причиной возникновения дорожно- транспортных происшествий, является усталость водителя, накапливающаяся во время поездки или присутствующая еще до ее начала. Распространенный признак усталости или утомления у водителя транспортного средства — зевота. Выявление признаков зевоты у человека способно охарактеризовать состояние усталости. Методы компьютерной обработки изображений активно применяются в задачах детектирования открытости рта и определения зевоты у человека. Однако такой подход обладает перечнем недостатков, к которым относятся различные окружающие условия работы и множество ситуационных вариантов зевоты для разных людей. Представлена схема детектора выявления признаков зевоты, заключающаяся в обработке изображений лица водителя с использованием методов анализа данных, компьютерной обработки изображений и сверточной модели нейронной сети. Метод. Сущность предложенного метода заключается в детектировании признаков состояния зевоты в поведении водителя в кабине транспортного средства на основе анализа последовательности изображений, полученных с видеокамеры. Показано, что возникновение состояния зевоты водителя сопровождается широким и продолжительным открытием рта. Длительная открытость рта сигнализирует о возникновении признаков зевоты у человека. Представлена концептуальная модель обнаружения открытости рта у человека, и разработана схема обработки и полуавтоматической разметки наборов данных YawDD и Kaggle Drowsiness Dataset. Разработанная модель сверточной нейронной сети показала точность равную 0,992 и полноту — 0,871 на тестовом 10 % наборе данных. Основные результаты. Предложенная схема детектора выявления призраков зевоты апробирована на тестовой выборке видео, сформированной набором данных YawDD: Yawning Detection Dataset. Данная схема детектирования успешно обнаружила 124 зевка среди всех видеофайлов из тестового набора данных. Доля правильно классифицированных объектов составила 98,2 %, точность — 96,1 %, полнота — 98,4 %, а F-мера — 97,3 % при определении признаков зевоты у водителя транспортного средства. Практическая значимость. Детектирование признаков зевоты в поведении водителя позволяет уточнить о нем информацию, и тем самым повысить эффективность существующих систем мониторинга водителя в кабине транспортного средства, ориентированных на предотвращение и снижение риска возникновения дорожно-транспортных происшествий. Предлагаемый подход может быть использован совместно с другими технологиями мониторинга поведения водителя при построении интеллектуальной системы поддержки водителя.

Among the factors that usually cause road accidents in the world is driver fatigue, which accumulates during the trip or is present even before it begins. One of the most common signs of fatigue or tiredness of a vehicle driver is yawning. The detection of signs of yawning in human behavior is potentially able to further characterize its state of fatigue. Computer image processing methods are actively used to detect the openness of the mouth and yawning for a person. However, this approach has many disadvantages, which include different environmental conditions and a variety of situational yawning options for different people. The paper presents a scheme of a detector for determining signs of yawning, which is focused on processing images of the driver’s face using data analysis methods, computer image processing, and a convolutional neural network model. The essence of the proposed method is to detect yawning in the driver’s behavior in the cabin of a vehicle based on the analysis of a sequence of images obtained from a video camera. It is shown that the driver’s yawning state is accompanied by a wide and prolonged openness of the mouth. Prolonged openness of the mouth signals the appearance of signs of yawning. A conceptual model for detecting the openness of the mouth for a vehicle driver is presented and a scheme for processing and labeling the YawDD and Kaggle Drowsiness Dataset datasets is developed. The developed convolutional neural network model showed an accuracy of 0.992 and recall of 0.871 on a test 10 % data set. The proposed scheme for detecting the yawning state has been validated on a test video subset extracted from the YawDD: Yawning Detection Dataset. This detection scheme successfully detected 124 yawns among all video files from the test dataset. The proportion of correctly classified objects is 98.2 % accuracy, precision is equal to 96.1 %, recall is 98.4 %, and F score is 97.3 % while detecting signs of yawning in driver behavior. Detecting signs of yawning in the driver’s behavior allows one to clarify information about the driver and thereby to increase the effectiveness of existing driver monitoring systems in the vehicle cabin, aimed at preventing and reducing the risk of road accidents. The proposed approach can be combined with other technologies for monitoring driver behavior when building an intelligent driver support system.

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