A multi-feature fusion algorithm for driver fatigue detection based on a lightweight convolutional neural network

The majority of the current widely used algorithms for fatigue detection rely on shallow learning to extract fatigue characteristics and use a single feature to determine the level of fatigue. The accuracy of detection is greatly affected by individual and environmental differences, and there are ce...

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Bibliographic Details
Published inThe Visual computer Vol. 40; no. 4; pp. 2419 - 2441
Main Authors Cheng, Wangfeng, Wang, Xuanyao, Mao, Bangguo
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2024
Springer Nature B.V
Subjects
Algorithms
Artificial Intelligence
Artificial neural networks
Computer Graphics
Computer Science
Driver fatigue
Fatigue
Heart rate
Image Processing and Computer Vision
Lane changing
Lightweight
Mouth
Neural networks
Original Article
Physiology
Roads & highways
Sensors
Traffic
Vehicles
Warning systems
Fatigue detection
Multi-feature fusion
Pre-warning system
Lightweight convolutional neural network
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Summary:The majority of the current widely used algorithms for fatigue detection rely on shallow learning to extract fatigue characteristics and use a single feature to determine the level of fatigue. The accuracy of detection is greatly affected by individual and environmental differences, and there are certain limitations in complex scenes. To improve the accuracy and real-time performance of the fatigue detection algorithm, a new driver fatigue detection algorithm based on multi-feature fusion is proposed. This paper employs two cameras to capture photos of the driver and the road, respectively, and a lightweight convolutional neural network to extract features from the driver's face, including the eyes, mouth, and head, as well as lane departure features from the road images. The four fatigue features are analyzed and fused to comprehensively detect the driver's fatigue state. The experimental results show that the multi-feature fusion-based driver fatigue detection algorithm can not only detect the driver's fatigue condition accurately but also classify the fatigue state according to the degree of fatigue, which is useful for making effective pre-warning system.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0178-2789
1432-2315
DOI:10.1007/s00371-023-02927-6