Artificial Intelligence-Based Real-Time Driver Drowsiness Detection: A Hybrid Model Approach

Abstract

This study presents the development of a deep learning-based model designed to detect fatigue and drowsiness of drivers in real time. Utilizing open-access datasets such as the MRL Eye Dataset and the Yawn Dataset, the model performs an in-depth analysis of eye open/closed states and yawning behaviors of drivers. The MRL Eye Dataset, containing approximately 85.000 images, is used to classify eye states, while the Yawn Dataset, with over 5.000 images, supports the detection of yawning movements. To enhance model accuracy, these datasets are balanced and optimized through various preprocessing techniques. The model is trained using Convolutional Neural Networks (CNNs) and further improved through transfer learning methods, significantly increasing classification performance. The proposed model achieves impressive metrics with an accuracy of 98%, a precision of 97.5%, and a specificity of 98.2%, indicating high performance. By focusing on blink and yawn detection, this study offers a novel approach compared to existing literature and provides a more reliable and effective solution to detect driver fatigue. Moreover, the use of synthetic data allows the model to be trained with broader and more diverse datasets, overcoming the limitations of real data collection. In future work, incorporating additional biometric indicators such as head movements and facial expressions could further improve the model’s accuracy and enable a more comprehensive assessment of driver alertness. Additionally, the generalizability of the model can be enhanced by including data from different cultural and geographical groups, thereby extending its applicability to a wider range of users. In conclusion, the proposed deep learning-based model demonstrates significant potential in transforming traffic safety by effectively detecting driver fatigue. With the advancement of technologies such as autonomous vehicles and intelligent transportation systems, this model could be integrated into driver assistance systems to help prevent accidents and enhance guest safety. It represents a revolutionary step toward preventing traffic accidents and ensuring driver well-being.

Keywords

• Shallow water equation
• reduced order modeling
• proper orthogonal decomposition

Yapay Zekâ Tabanlı Gerçek Zamanlı Sürücü Uyuşukluğu Tespiti: Hibrit Model Yaklaşımı

Abstract

Sürücülerin yorgunluk ve uyuşukluk durumlarını gerçek zamanlı olarak tespit etmeyi amaçlayan derin öğrenme tabanlı bir model geliştirmektedir. MRL Eye Dataset ve Yawn Dataset gibi açık erişimli veri setlerinden faydalanarak, sürücülerin göz açık/kapalı durumları ve esneme hareketleri derinlemesine analiz edilmektedir. MRL Eye Dataset, yaklaşık 85.000 görüntü ile sürücünün göz durumlarını sınıflandırırken, Yawn Dataset ise 5.000’den fazla görüntü ile esneme hareketlerini tespit etmek için kullanılmaktadır. Bu veri setleri, modelin doğruluğunu artırmak amacıyla dengeli şekilde düzenlenmekte ve çeşitli ön işleme teknikleri ile iyileştirilmektedir. Model, CNN ile eğitilmekte ve transfer öğrenme teknikleriyle güçlendirilmekte, bu sayede modelin sınıflandırma başarısı önemli ölçüde artırılmaktadır. Modelin elde ettiği doğruluk oranı %98, hassasiyet oranı %97.5 ve özgüllük oranı ise %98.2 gibi yüksek metriklerle başarılı sonuçlar elde edilmektedir. Bu çalışma, literatürde ilk kez hem göz kırpma hem de esneme hareketlerini hibrit CNN tabanlı bir modelde birlikte ele almaktadır. Bu özgün yaklaşım, sürücü yorgunluğunu tespit etmede yalnızca tek parametreye odaklanan çalışmalara kıyasla daha kapsamlı ve güvenilir sonuçlar üretmektedir. Ayrıca, sentetik verilerin kullanılması, gerçek veri toplama zorluklarını aşarak daha geniş ve çeşitlendirilmiş veri setleriyle modelin eğitilmesine olanak tanımaktadır. Gelecekte, baş hareketleri, yüz ifadeleri ve diğer biyometrik verilerin sisteme entegre edilmesi ile modelin doğruluğu daha da artırılabilir ve sürücülerin dikkat seviyelerini daha kapsamlı bir şekilde değerlendirebilir. Ayrıca, farklı kültürel ve coğrafi gruplardan elde edilen verilerle modelin genellenebilirliği sağlanarak, daha geniş bir kullanıcı kitlesine hitap edilmesi mümkün hale gelebilmektedir. Sonuç olarak, bu çalışma, sürücü yorgunluğunu tespit etmeye yönelik geliştirilen derin öğrenme tabanlı modelin, trafik güvenliğini derinlemesine dönüştürebilecek büyük bir potansiyele sahip olduğunu ve yol güvenliğini önemli ölçüde artırabileceğini ortaya koymaktadır. Özellikle otonom araçlar ve akıllı ulaşım sistemleri gibi teknolojilerin gelişimiyle paralel olarak, bu model sürücü destek sistemlerine entegre edilerek, kazaların önlenmesine katkı sağlayabilir ve sürücülerin güvenliğini artırabilir. Bu model, trafik kazalarını önlemek ve sürücülerinin güvenliğini sağlamak adına devrim niteliğinde bir adım olarak değerlendirilebilmektedir.

Keywords

• Shallow water denklemi
• mertebe indirgenmiş modelleme
• düzgün dik ayrışma

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