Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images

Magán, Elena; Sesmero, M. Paz; Alonso-Weber, Juan Manuel; Sanchís, Araceli

doi:10.3390/app12031145

Cited by 74 publications

(29 citation statements)

References 39 publications

(55 reference statements)

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“…The study in [3] created an advanced driver assistance system (ADAS) that focused on identifying motorists' levels of intoxication and warning them to prevent collisions. It is prevalent for drivers to get fatigued and detecting such a state in an intrusive way is extremely important.…”

Section: Literature Reviewmentioning

confidence: 99%

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Li¹,

Sun²,

Hu³

2023

Computers, Materials &Amp; Continua

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Driver identification in intelligent transport systems has immense demand, considering the safety and convenience of traveling in a vehicle. The rapid growth of driver assistance systems (DAS) and driver identification system propels the need for understanding the root causes of automobile accidents. Also, in the case of insurance, it is necessary to track the number of drivers who commonly drive a car in terms of insurance pricing. It is observed that drivers with frequent records of paying "fines" are compelled to pay higher insurance payments than drivers without any penalty records. Thus driver identification act as an important information source for the intelligent transport system. This study focuses on a similar objective to implement a machine learning-based approach for driver identification. Raw data is collected from in-vehicle sensors using the controller area network (CAN) and then converted to binary form using a one-hot encoding technique. Then, the transformed data is dimensionally reduced using the Principal Component Analysis (PCA) technique, and further optimal parameters from the dataset are selected using Whale Optimization Algorithm (WOA). The most relevant features are selected and then fed into a Convolutional Neural Network (CNN) model. The proposed model is evaluated against four different use cases of driver behavior. The results show that the best prediction accuracy is achieved in the case of drivers without glasses. The proposed model yielded optimal accuracy when evaluated against the K-Nearest Neighbors (KNN) and Support Vector Machines (SVM) models with and without using dimensionality reduction approaches.

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Section: Literature Reviewmentioning

confidence: 99%

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Li¹,

Sun²,

Hu³

2023

Computers, Materials &Amp; Continua

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“…Et.al Elena Magan [10] this research paper involves developing an ADAS (advanced driver assistance system) that focuses on detecting driver sleepiness to prevent road accidents. The system assesses drivers' drowsiness using non-intrusive 60-second-long videos of their faces.…”

Section: Et Al Serajeddin Ebrahimianmentioning

confidence: 99%

In Cabin Driver Monitoring and Alerting System For Passenger cars using Machine Learning.

Suryavanshi,

Ladhwe,

Joshi

2023

J. Phys.: Conf. Ser.

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The primary causes of road accidents are driver errors. Such as excessive speeding, driver distraction, and driver sleepiness. Diver sleepiness and distraction can be controlled by implementing advanced technology such as an in-cabin driver monitoring system. This is a technological solution that uses a machine learning algorithm to improve road safety. This paper describes a new real-time driver monitoring and alerting system that solely monitors driver Sleepiness, diver distraction, and seatbelt wearing status in order to prevent road accidents. It employs the YOLOv5 deep learning algorithm to detect various types of distraction and For seatbelt wearing status and computer vision’s cvzone framework is used to detect sleepiness using 3D facial landmarking. In this system, The camera is mounted in the centre of the vehicle’s dashboard for data collection and real-time implementation. The Raspberry Pi 3b module is used for the Realtime implementation. According to the findings of this study, the system is capable of real-time implementation, and the machine learning model used in the study achieves an accuracy of more than 90%.

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“…The first alternative uses a recurrent and convolutional neural network, while the second one uses deep learning techniques to extract numeric features from images, which are introduced into a fuzzy logic-based system afterwards. [3] Deep Learning for Eye Blink Detection Implemented at the Edge This innovative solution is compared with a more traditional method, based on a detection threshold mechanism. The performance, battery lifetime and memory footprint of both solutions are assessed for embedded implementation in connected glasses.…”

Section: IImentioning

confidence: 99%

Real Time Drowsiness Detection System Using CNN

Pushkar¹,

Khandare²,

Prasad³

et al. 2023

IJRASET

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Driver fatigue and rash driving are the leading causes of road accidents, which result in the loss of valued life and decrease road traffic safety. Driver drowsiness solutions that are reliable and precise are essential to prevent accidents and increase road traffic safety. Various driver drowsiness detection systems have been developed using various technologies that are geared at the specific parameter of detecting the driver's tiredness. This research offers a unique multi-level distribution model for detecting driver drowsiness utilising Convolution Neural Networks (CNN) and. To detect the driver's behaviour and emotion, the driver's face pattern is handled with a 2D Convolution Neural Network (CNN). The suggested model is built with OpenCV, and the experimental findings show that it recognises the driver's emotion and tiredness more efficiently than existing technologies

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Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images

Cited by 74 publications

References 39 publications

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

Whale Optimization Algorithm-Based Deep Learning Model for Driver Identification in Intelligent Transport Systems

In Cabin Driver Monitoring and Alerting System For Passenger cars using Machine Learning.

Real Time Drowsiness Detection System Using CNN

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