A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification

Florez, Ruben; Palomino-Quispe, Facundo; Coaquira-Castillo, Roger Jesus; Herrera-Levano, Julio Cesar; Paixão, Thuanne; Alvarez, Ana Beatriz

doi:10.3390/app13137849

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…Ebrahimian et al [55] have integrated CNN with LSTM in order to classify the driver's drowsiness considering the rate of respiration, variability of heart rate as well as its power spectrum. Adoption of CNN is also witnessed in work of Florez et al [56] and Jahan et al [57] where the detection of drowsiness state of the driver is carried out using eye extraction method adopting multiple neural network model. The discussion presented by Khan et al [58] have developed an experimental setup for assessing the drowsiness for multiple drivers using Android mobile application.…”

Section: Existing Studies Towards Driver's Drowsiness Detectionmentioning

confidence: 99%

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

Shankara Chari,

Prashant

2024

IJ-AI

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The state of fatigueness and drowsiness relates to the stressed physical and mental condition of a driver that reduces the ability of a driver to drive safely leading to fatal consequences of road accidents. With a rising concerns about the road safety, the premium and modern vehicles are coming up with a sophisticated technology to detect and rise alarm during the positive case of fatigueness and drowsiness. Irrespective of availability of archives of literatures towards solving this problem, it is quite unclear about the strength and weakness of varied methodologies. Therefore, this paper presents a crisp and insightful discussion about the recent methodologies associated with detecting driver's attention, fatigueness, drowsiness along with highlights of commercial devices to realize various limiting factors and constraints associated with them. The paper contributes to introduce a well-structured flow of research trend to realize various patterns of current trend adopted towards solving varied problems and significant research gaps have been identified in this process. The outcome of this paper presents that still there is an open scope of an improvement towards accomplishing the agenda towards safer driving.

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Section: Existing Studies Towards Driver's Drowsiness Detectionmentioning

confidence: 99%

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

Shankara Chari,

Prashant

2024

IJ-AI

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“…The research study [9] aims to detect driver drowsiness through eye detection. In this experiment, the NITYMED dataset is utilized, which contains various levels of drowsiness and videos of drivers.…”

Section: Literature Analysismentioning

confidence: 99%

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Madni,

Raza,

Sehar

et al. 2024

IEEE Access

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Driver drowsiness detection is a critical field of research within automotive safety, aimed at identifying signs of fatigue in drivers to prevent accidents. Drowsiness impairs a driver's reaction time, decision-making ability, and overall alertness, significantly increasing the risk of collisions. Nowadays, the challenge is to detect drowsiness using physiological signals, which often require direct contact with the driver's body. This can be uncomfortable and distracting. This study aimed at detecting driver drowsiness through eye movement behavior imagery of the driver. We utilized a standard image dataset based on the eye movement behavior of drivers to conduct this research experiment. We proposed a novel transfer learning-based features generation which combined the strengths of the Visual Geometry Group (VGG-16) and Light Gradient-Boosting Machine (LGBM) methods. The proposed VGLG (VGG16-LGBM) approach first extracts spatial features from input eye image data and then generates salient transfer features using LGBM. Experimental evaluations reveal that the k-neighbors classifier outperformed the state-of-the-art approach with a high-performance accuracy of 99%. The computational complexity analysis shows that the proposed approach detects driver drowsiness in 0.00829 seconds. We have enhanced the performance through hyperparameter tuning and validations using k-fold validation. This research has the potential to revolutionize driver drowsiness detection, aiming to prevent road accidents and save precious lives.

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“…Alternatively, a Convolutional Neural Network (CNN) can be used with the InceptionV3, VGG16, ResNet50V2 model architectures to detect sleepiness in real-time via the camera. The performance of the three models can be compared to determine the most effective method [9]. The research proposes a method to detect drowsiness using a camera that captures the driver's eyes in real-time.…”

Section: Introductionmentioning

confidence: 99%

Drowsiness Detection in the Advanced Driver-Assistance System using YOLO V5 Detection Model

Fauzan Ridho,

Fransiskus Panca,

Welly Yandi

et al. 2024

ECT

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The development of Artificial Intelligence (AI) in the automobile industry, particularly in the Advanced Driver-Assistance System (ADAS), has been increasing rapidly in recent years. One of the essential features of ADAS is the drowsiness alert system, which monitors the state of the driver's eyes. This article presents a study on the development of ADAS that focuses on drowsiness detection using deep learning through a Convolutional Neural Network (CNN) approach. The study utilized the YOLO V5 model for object detection, which was trained using custom datasets containing images annotated with two labels: "Mengantuk" (Drowsy) and "Bangun" (Awake) The goal was to recognize whether the driver was drowsy or awake. The results of the study showed that the modified YOLO V5 CNN Model had high accuracy based on evaluation metrics in terms of accuracy, precision, and recall in detecting drowsiness around the area of the eyes, with a Mean Average Precision (mAP) of 99.5% and an F1-Score of 99.8%. For better understanding and visualization, the model was tested using real-time detection through a web camera, using Jetson Nano as the inference device. The model detected drowsiness in real time, with a confidence rate of 80% to 97%

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A CNN-Based Approach for Driver Drowsiness Detection by Real-Time Eye State Identification

Cited by 17 publications

References 27 publications

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

Novel Transfer Learning Approach for Driver Drowsiness Detection Using Eye Movement Behavior

Drowsiness Detection in the Advanced Driver-Assistance System using YOLO V5 Detection Model

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