Convolutional neural network-based real-time drowsy driver detection for accident prevention

Nippon Datta (Chittagong University of Engineering and Technology)
Tanjim Mahmud (Rangamati Science and Technology University)
Manoara Begum (Port City International University)
Mohammad Tarek Aziz (Chittagong University of Engineering and Technology)
Dilshad Islam (Chattogram Veterinary and Animal Sciences University)
Md. Faisal Bin Abdul Aziz (Comilla University)
Khudaybergen Kochkarov (Tashkent State University of Economy)
Temur Eshchanov (Urgech State University Named After Abu Rayhon Beruni)
Valisher Sapayev Odilbek Uglu (Mamun University)
Sobir Parmanov (National University of Uzbekistan)
Mohammad Shahadat Hossain (University of Chittagong)
Karl Andersson (Lulea University of Technology)

TELKOMNIKA (Telecommunication Computing Electronics and Control)
Vol 23, No 3: June 2025

Drowsy driving significantly threatens road safety, contributing to many accidents globally. This paper presents a convolutional neural network (CNN)-based real-time drowsy driver detection system aimed at preventing such accidents, particularly for deployment in Android applications. We propose a lightweight CNN architecture that effectively identifies drowsiness and microsleep episodes by categorizing driver facial expressions into four distinct categories: close-eye expressions, open-eye expressions, yawns, and no yawns. Our model, which employs facial landmark detection and various pre-processing techniques to enhance accuracy, achieves an impressive 96.6% accuracy. This performance surpasses several popular CNN architectures, including VGG16, VGG19, MobileNetV2, ResNet50, and DenseNet121. Notably, our proposed model is highly efficient, with only 0.4 million parameters and a memory requirement of 1.51 MB, making it ideal for real-time applications. The comparative analysis highlights the superior balance between accuracy and resource efficiency of our model, demonstrating its potential for practical deployment in reducing accidents caused by driver fatigue.