Driver’s Drowsiness Detection System Using Dlib HOG

Babu, Athira; Nair, Shruti; Sreekumar, K

doi:10.1007/978-981-16-3675-2_16

Cited by 10 publications

(3 citation statements)

References 11 publications

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“…Results showed high accuracy of 98.962% for eye state classification and 98.561% for mouth state classification on a dataset developed for this study. [4] Describes a drowsiness identification model that integrates face and head pose detection using Dlib models. Recognition of driver fatigue through detecting yawns [20][19] [31].…”

Section: Introductionmentioning

confidence: 99%

An Approach to Hyperparameter Tuning in Transfer Learning for Driver Drowsiness Detection Based on Bayesian Optimization and Random Search

Vo¹,

Ngoc²,

Quach³

2023

IJACSA

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Driver drowsiness is a critical factor in road safety, and developing accurate models for detecting it is essential. Transfer learning has been shown to be an effective technique for driver drowsiness detection, as it enables models to leverage large, pre-existing datasets. However, the optimization of hyperparameters in transfer learning models can be challenging, as it involves a large search space. The core purpose of this research is on presenting an approach to hyperparameter tuning in transfer learning for driving fatigue detection based on Bayesian optimization and Random search algorithms. We examine the efficiency of our approach on a publicly available dataset using transfer learning models with the MobileNetV2, Xception, and VGG19 architectures. We explore the impact of hyperparameters such as dropout rate, activation function, the number of units (the number of dense nodes), optimizer, and learning rate on the transfer learning models' overall performance. Our experiments show that our approach improves the performance of the transfer learning models, obtaining cutting-edge results on the dataset for all three architectures. We also compare the efficiency of Bayesian optimization and Random search algorithms in terms of their ability to find optimal hyperparameters and indicate that Bayesian optimization is more efficient in finding optimal hyperparameters than Random search. The results of our study provide insights into the importance of hyperparameter tuning for transfer learning-based driver drowsiness detection using different transfer learning models and can guide the selection of hyperparameters and models for future studies in this field. Our proposed approach can be applied to other transfer learning tasks, making it a valuable contribution to the field of ML.

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Section: Introductionmentioning

confidence: 99%

An Approach to Hyperparameter Tuning in Transfer Learning for Driver Drowsiness Detection Based on Bayesian Optimization and Random Search

Vo¹,

Ngoc²,

Quach³

2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…Currently, research on driver-fatigue detection mainly focuses on the field of road traffic and can be divided into three methods: detection based on vehicle driving characteristics [3][4][5], detection based on driver physiological characteristics [6][7][8], and detection based on computer vision of driver facial features [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Among them, visual-based detection uses cameras or other image sensors to capture the facial-feature changes or headmovement information of the driver.…”

Section: Introductionmentioning

confidence: 99%

“…However, the weight model of YOLOv3-tiny is still not lightweight enough and needs further optimization for lightweight deployment on in-vehicle terminals. Furthermore, Babu et al [21] have developed a drowsiness recognition system using Python and Dlib, which includes face detection and head-pose detection. It achieved a 94.51% accuracy in real-time video detection.…”

Section: Introductionmentioning

confidence: 99%

Research on Fatigued-Driving Detection Method by Integrating Lightweight YOLOv5s and Facial 3D Keypoints

Ran,

He,

2023

Sensors

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In response to the problem of high computational and parameter requirements of fatigued-driving detection models, as well as weak facial-feature keypoint extraction capability, this paper proposes a lightweight and real-time fatigued-driving detection model based on an improved YOLOv5s and Attention Mesh 3D keypoint extraction method. The main strategies are as follows: (1) Using Shufflenetv2_BD to reconstruct the Backbone network to reduce parameter complexity and computational load. (2) Introducing and improving the fusion method of the Cross-scale Aggregation Module (CAM) between the Backbone and Neck networks to reduce information loss in shallow features of closed-eyes and closed-mouth categories. (3) Building a lightweight Context Information Fusion Module by combining the Efficient Multi-Scale Module (EAM) and Depthwise Over-Parameterized Convolution (DoConv) to enhance the Neck network’s ability to extract facial features. (4) Redefining the loss function using Wise-IoU (WIoU) to accelerate model convergence. Finally, the fatigued-driving detection model is constructed by combining the classification detection results with the thresholds of continuous closed-eye frames, continuous yawning frames, and PERCLOS (Percentage of Eyelid Closure over the Pupil over Time) of eyes and mouth. Under the premise that the number of parameters and the size of the baseline model are reduced by 58% and 56.3%, respectively, and the floating point computation is only 5.9 GFLOPs, the average accuracy of the baseline model is increased by 1%, and the Fatigued-recognition rate is 96.3%, which proves that the proposed algorithm can achieve accurate and stable real-time detection while lightweight. It provides strong support for the lightweight deployment of vehicle terminals.

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Digital Wah-Wah Guitar Effect Controlled by Mouth Movements

Nowosielski

Reginia

2023

Lecture Notes in Networks and Systems

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Driver’s Drowsiness Detection System Using Dlib HOG

Cited by 10 publications

References 11 publications

An Approach to Hyperparameter Tuning in Transfer Learning for Driver Drowsiness Detection Based on Bayesian Optimization and Random Search

An Approach to Hyperparameter Tuning in Transfer Learning for Driver Drowsiness Detection Based on Bayesian Optimization and Random Search

Research on Fatigued-Driving Detection Method by Integrating Lightweight YOLOv5s and Facial 3D Keypoints

Digital Wah-Wah Guitar Effect Controlled by Mouth Movements

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