Driver Drowsiness Detection System using Convolutional Neural Network

Walizad, Mohammad Elham; Hurroo, Mehreen; Sethia, Divyashikha

doi:10.1109/icoei53556.2022.9777182

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…The model detects drowsiness and alarms the driver to take safety measures. It achieves an overall accuracy of 95%, outperforming all previous studies on drowsiness detection [16].…”

Section: Literature Reviewmentioning

confidence: 70%

Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach

Adarsh

Singh

2023

J. Infor. Electr. Electron. Eng.

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The process of determining if a person, generally a driver, is becoming sleepy or drowsy while performing a task such as driving is known as drowsiness detection. It is a necessary system for detecting and alerting drivers to their tiredness, which might impair their driving ability and lead to accidents. The project aims to create a reliable and efficient system capable of real-time detection of drowsiness using OpenCV, Dlib, and facial landmark detection technologies. The project's results show that the sleepiness detection method can accurately and precisely identify tiredness in real time. The technology is less intrusive and more economical than conventional sleepiness detection techniques. The system is based on a 68 facial landmark detector, which is a highly trained and effective detector capable of recognizing human face points. The detector aids in assessing whether the driver's eyes are closed or open. The system analyses the data collected by the detector using machine learning methods to discover patterns associated with drowsiness. When drowsiness is detected, the system incorporates a warning mechanism, such as an alarm or a vibration in the steering wheel, to notify the driver. A variety of studies with different drivers and driving conditions were used to evaluate the performance of the real-time driver drowsiness detection system. The results show that the technology can detect tiredness properly and deliver timely warnings to the driver. This method can assist in preventing drowsy driving incidents, enhancing road safety, and saving lives. The results indicated that the algorithm had an average accuracy rate of 94% for identifying tiredness in drivers.

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“…The model detects drowsiness and alarms the driver to take safety measures. It achieves an overall accuracy of 95%, outperforming all previous studies on drowsiness detection [16].…”

Section: Literature Reviewmentioning

confidence: 70%

Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach

Adarsh

Singh

2023

J. Infor. Electr. Electron. Eng.

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“…A laptop camera with a resolution of 640 x 480 at 30 frames per second is employed in this setup. The number of frames set for each warning is 20 frames, and these values are intended to prevent needless notifications when a user's eyes are naturally closing due to blinking, or any other facial emotion [13]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…Table 4 shows the evaluation metrics of the model. Table 5 shows the results compared between our model, and a model trained using YawDD dataset [13] to detect mouth and eyes. Our model has the highest set of accuracy and precision; however, our model is considered the least among the other models if comparing the recall score.…”

Section: Resultsmentioning

confidence: 99%

Driver's Fatigue Recognition Using Convolutional Neural Network Approach

Abbas

Tiang

Lim

et al. 2023

Proceedings of International Conference on Artificial Life and Robotics

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Drowsy driving is a serious issue that has been leaking in our communities since long time, the definition of drowsy driving is when the driver is not aware enough to proceed with driving the vehicle causing catastrophic accidents. Multiple methods were found to approach this complication across the years. Convolution Neural Network has approved to be a reliable approach to treat this issue by using face feature detection. In this paper, the effect of key parameters of the trained framework based on the driver's fatigue recognition model are analyzed, and the accuracy of the driver's fatigue recognition model is investigated, as well as a driver's fatigue recognition is studied under different conditions using CNN. Transfer learning is used to develop a reliable method for detection, Mediapip Face Mesh model is used to extract the features from the face. MAR (Mouth Aspect Ratio) as well as EAR (Eyes Aspect Ratio) are obtained through the detection, these terms are responsible for detecting the eye and mouth closure ratio, the model has proved to work with accuracy of 98.3% and in different light conditions with accuracy of 94.7% outperforming several past models.

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“…It is based on the VGG19 architecture with modified hyperparameters. The network is evaluated on the MRL dataset and presented better results than others [12,13] in the literature.…”

Section: Appearance-based Methodsmentioning

confidence: 97%

BlinkLinMulT: Transformer-Based Eye Blink Detection

Fodor,

Fenech,

Lőrincz

2023

J. Imaging

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This work presents BlinkLinMulT, a transformer-based framework for eye blink detection. While most existing approaches rely on frame-wise eye state classification, recent advancements in transformer-based sequence models have not been explored in the blink detection literature. Our approach effectively combines low- and high-level feature sequences with linear complexity cross-modal attention mechanisms and addresses challenges such as lighting changes and a wide range of head poses. Our work is the first to leverage the transformer architecture for blink presence detection and eye state recognition while successfully implementing an efficient fusion of input features. In our experiments, we utilized several publicly available benchmark datasets (CEW, ZJU, MRL Eye, RT-BENE, EyeBlink8, Researcher’s Night, and TalkingFace) to extensively show the state-of-the-art performance and generalization capability of our trained model. We hope the proposed method can serve as a new baseline for further research.

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Driver Drowsiness Detection System using Convolutional Neural Network

Cited by 9 publications

References 13 publications

Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach

Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach

Driver's Fatigue Recognition Using Convolutional Neural Network Approach

BlinkLinMulT: Transformer-Based Eye Blink Detection

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