Computer vision-based approach to detect fatigue driving and face mask for edge computing device

Rahman, Asifur; Hriday, Mamun Bin Harun; Khan, Riasat

doi:10.1016/j.heliyon.2022.e11204

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…ResNet50 achieved the best model for drowsiness detection based on yawning conditions. This widely used model performs better in image classification than other pre-trained models [11,13,14]. The advantage of the pre-trained model ResNet50 is that performance does not decrease even though the architecture is modified deeper.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…However, this development is still centered on deepening the facial characteristics of drowsy drivers and machine learning classification. Similar research with a machine and deep learning algorithms can be found in [13,14,2]. The novelty of this research aims to build on previous research by producing a transfer learning model which can detect drowsy drivers based on yawning.…”

Section: Introductionmentioning

confidence: 90%

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Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

Ilmadina,

Naufal,

Wibowo

2023

matrik

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Traffic accidents are fatal events that need special attention. According to research by the National Transportation Safety Committee, 80% of traffic accidents are caused by human error, one of which is tired and drowsy drivers. The brain can interpret the vital fatigue of a drowsy driver sign as yawning. Therefore, yawning detection for preventing drowsy drivers’ imprudent can be developed using computer vision. This method is easy to implement and does not affect the driver when handling a vehicle. The research aimed to detect drowsy drivers based on facial expression changes of yawning by combining the Haar Cascade classifier and a modified pre-trained model, MobileNetV2 and ResNet50. Both proposed models accurately detected real-time images using a camera. The analysis showed that the yawning detection model based on the ResNet50 algorithm is more reliable, with the model obtaining 99% of accuracy. Furthermore, ResNet50 demonstrated reproducible outcomes for yawning detection, considering having good training capabilities and overall evaluation results.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 90%

Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

Ilmadina,

Naufal,

Wibowo

2023

matrik

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“…Others use sensors and cameras in a vehicle to collect information about the driver's behavior patterns and vehicle controls to help identify fatigue. However, the application of physiological parameters has significant noise processing problems, and facial image analysis of cameras in the vehicle cabin has human privacy issues and lighting environment limitations [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Fatigue in Older Drivers Using a Multimodal Medical Sensor and Driving Simulator

Yoshida,

Kowata,

Abe

et al. 2024

Electronics

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In recent years, the spread of wearable medical sensors has made it possible to easily measure biological signals such as pulse rate and body acceleration (BA), and from these biological signals, it is possible to evaluate the degree of biological stress and autonomic nervous activity in daily life. Accumulated fatigue due to all-day work and lack of sleep is thought to be a contributing factor to distracted driving, and technology to estimate fatigue from biological signals during driving is desired. In this study, we investigated fatigue evaluation during a driving simulator (DS) using biological information on seven older subjects. A DS experiment was performed in the morning and afternoon, and no significant differences were observed in the change over time of heart rate variability and skin temperature. On the other hand, in the afternoon DS, changes in arousal and body movements were observed based on BA calculated from the three-axis acceleration sensor and fingertip reaction time in a psychomotor vigilance test. It is suggested that by combining biological information, it may be possible to evaluate the degree of fatigue from the presence or absence of arousal and changes in body movements while driving.

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“…Fatigued driving is a significant cause of traffic accidents [4,18,19]. Thus, detecting fatigued driving is an effective approach to preventing traffic accidents [20,21].…”

Section: Introductionmentioning

confidence: 99%

“…A functional brain network was constructed to recognize the fatigued driving state, and the neural mechanism of fatigued driving was analyzed [22]. A complete embedded system was presented to detect fatigued driving using deep learning, computer vision, and heart rate monitoring [19]. A fatigued driving recognition framework was proposed, which could denoise the electroencephalogram signals based on a deep convolutional neural network and the dynamically constructed functional brain network [23].…”

Section: Introductionmentioning

confidence: 99%

Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data

Yang

2024

Sustainability

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Dangerous or illegal driving may disrupt the traffic safety management of public security organs, damage road infrastructure, lead to traffic accidents, or result in economic losses. This paper proposes a framework based on China’s highway toll data to identify dangerous or illegal driving risks, such as unfamiliarity with road conditions, overload, driving over the speed limit, fatigued driving, fake license plates, and other risks. The unfamiliarity with road conditions is identified with the frequency of driving routes. When the total weight of a vehicle and its cargo is greater than the upper limit of the total weight of the vehicle and its cargo, the vehicle can be judged as overloaded. When the actual travel time is less than the minimum travel time, it can be inferred that the vehicle has a risk of fatigued driving, driving over the speed limit, a fake license plate, or other risks. Two accidents are used to demonstrate the process of the proposed framework for identifying driving risks based on China’s highway toll data. Additional analysis proves that the proposed framework can be used to identify dangerous or illegal driving risks, and it provides a scientific basis for the traffic safety management of public security organs, reducing infrastructure damage, and avoiding the loss of national taxes and fees.

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Computer vision-based approach to detect fatigue driving and face mask for edge computing device

Cited by 14 publications

References 40 publications

Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

Drowsiness Detection Based on Yawning Using Modified Pre-trained Model MobileNetV2 and ResNet50

Evaluation of Fatigue in Older Drivers Using a Multimodal Medical Sensor and Driving Simulator

Driving Risk Identification of Truck Drivers Based on China’s Highway Toll Data

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