title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÐµÐ¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development and research of machine learning models for a driver monitoring system
Creators: name: Ð¤ÐµÐ´Ð¾ÑÐ¾Ð²Ð°, ÐÐ»ÐµÐ½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2024
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð´Ð°ÑÐ°ÑÐµÑ
ÑÐ¸ÑÑÐµÐ¼Ð° Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð°
Ð»ÐµÐ½Ð´Ð¼Ð°ÑÐºÐ¸
CNN
YOLO
machine learning
neural network
dataset monitoring system
landmarks

description: ÐÐ°Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ ÑÐ¸ÑÑÐµÐ¼Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾ÑÐ²Ð»ÐµÑÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð²Ð¾Ð´Ð¸ÑÐµÐ»Ñ. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð¿ÑÐµÐ´Ð¼ÐµÑÐ½Ð°Ñ Ð¾Ð±Ð»Ð°ÑÑÑ, Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÑÑÐµÑÑÐ²ÑÑÑÐ¸Ñ ÑÐ¸ÑÑÐµÐ¼ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÐµÐ¹. Ð Ð°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ð¹ Ð´Ð»Ñ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð»ÐµÐ½Ð´Ð¼Ð°ÑÐºÐ¾Ð², ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ñ ÑÐ¼Ð¾ÑÐ¸Ð¹, ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð². Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ»Ð¸ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð´Ð¾ÑÑÑÐ¿Ð½ÑÐµ Ð´Ð°ÑÐ°ÑÐµÑÑ, Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ðµ Ð´Ð»Ñ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð½ÑÑ Ð·Ð°Ð´Ð°Ñ. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð´Ð»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð¾ÑÐ²Ð»ÐµÑÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð²Ð¾Ð´Ð¸ÑÐµÐ»Ñ. ÐÐ»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð·Ð°ÐºÑÑÑÑÑ Ð³Ð»Ð°Ð· Ð²Ð¾Ð´Ð¸ÑÐµÐ»Ñ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ, ÑÐ¾ÑÑÐ¾ÑÑÐ¸Ð¹ Ð¸Ð· ÑÐ¾Ð²Ð¾ÐºÑÐ¿Ð½Ð¾ÑÑÐ¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð¼ÐµÑÑÐ¸ÐºÐ¸ EAR Ð¿Ð¾ Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð½ÑÐ¼ Ð»ÐµÐ½Ð´Ð¼Ð°ÑÐºÐ°Ð¼ Ð³Ð»Ð°Ð· Ð¸ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð·Ð°ÐºÑÑÑÑÑ Ð³Ð»Ð°Ð·. ÐÐ»Ñ Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐ°Ð·Ð³Ð¾Ð²Ð¾ÑÐ° Ð¿Ð¾ ÑÐµÐ»ÐµÑÐ¾Ð½Ñ Ð±ÑÐ»Ð¸ Ð¾Ð±ÑÐµÐ´Ð¸Ð½ÐµÐ½Ñ Ð´Ð²Ð° Ð¿Ð¾Ð´ÑÐ¾Ð´Ð°: Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ðµ ÑÐ³Ð»Ð° ÑÐ³Ð¸Ð±Ð° Ð»Ð¾ÐºÑÑ Ð¿Ð¾ Ð»ÐµÐ½Ð´Ð°Ð¼Ð°ÑÐºÐ°Ð¼ Ð½Ð° ÑÐµÐ»Ðµ ÑÐµÐ»Ð¾Ð²ÐµÐºÐ° Ð¸ Ð´ÐµÑÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ ÑÐµÐ»ÐµÑÐ¾Ð½Ð° Ð² ÐºÐ°Ð´ÑÐµ.Â  ÐÐ»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð·ÐµÐ²Ð°Ð½Ð¸Ñ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð»ÐµÐ½Ð´Ð¼Ð°ÑÐºÐ¾Ð² ÑÑÐ° Ð¸ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ Ð¼ÐµÑÑÐ¸ÐºÐ¸ MAR Ð¾ÑÐºÑÑÑÐ¾ÑÑÐ¸ ÑÑÐ°. ÐÐ»Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ ÐºÑÑÐµÐ½Ð¸Ñ Ð±ÑÐ»Ð° ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð°, Ð¾Ð±ÑÑÐµÐ½Ð° Ð¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° Ð¾Ð´Ð½Ð¾Ð¿ÑÐ¾ÑÐ¾Ð´Ð½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ YOLO. ÐÐ»Ñ ÐºÐ»Ð°ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸ ÑÐ¼Ð¾ÑÐ¸Ð¹ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð¾Ð¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸ DAN Ð¸ EfficientNet. ÐÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¾ Ð¾Ð¿Ð¸ÑÐ°Ð½Ð¸Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð½ÑÑ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð². Ð¢Ð°ÐºÐ¶Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½Ð°Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð±ÑÐ»Ð° Ð¿Ð¾Ð»ÑÑÐµÐ½Ð° ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð°Ñ ÑÐ¸ÑÑÐµÐ¼Ð°, ÐºÐ¾ÑÐ¾ÑÐ°Ñ ÑÐ¾ÑÐ¾ÑÐ¾ Ð¿Ð¾ÐºÐ°Ð·Ð°Ð»Ð° ÑÐµÐ±Ñ Ð¿ÑÐ¸ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ð¸.
description: This work is devoted to the development of a system for determining driver distraction. During the work, the subject area was analyzed and an analysis of existing driver monitoring systems was carried out. Machine learning models for constructing landmarks, emotion recognition, image classification and object detection are considered. Available datasets suitable for the described tasks were also considered. Algorithms have been developed to determine driver distraction. To determine the drivers closed eyes, a calculation algorithm was developed, consisting of a combination of an algorithm using the EAR metric based on the constructed eye landscapes and a closed eyes detection model. To detect a telephone conversation, two approaches were combined: calculating the bend angle of the elbow using landmarks on the human body and detecting a telephone in the frame. To determine yawning, an algorithm was developed using mouth landscapes and calculating the MAR metric of mouth openness. To detect smoking, a single-pass neural network YOLO was developed, trained and tested. To classify emotions, an approach was developed using the convolutional neural network DAN and EfficientNet. A description of the developed algorithms is presented. A software implementation has also been developed. The result was an effective system that performed well during testing.