Behavior-based driver fatigue detection system with deep belief network

Kır Savaş B., Becerikli Y.

NEURAL COMPUTING AND APPLICATIONS, cilt.1, ss.14053-14065, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s00521-022-07141-4
  • Dergi Adı: NEURAL COMPUTING AND APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Sayfa Sayıları: ss.14053-14065
  • Anahtar Kelimeler: Driver fatigue detection, Deep learning, DBN, RBM, Image processing, DROWSINESS DETECTION, POWER
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Traffic accidents as a result of driver fatigue and drowsiness have caused many injuries and deaths. Therefore, driver fatigue detection and prediction system have been recognized as important potential research areas to prevent accidents caused by fatigue and drowsiness while driving. In this study, driver fatigue is determined by using behavior-based measurement information. Recent studies show that deep neural network is trending state-of-the-art machine learning approaches. Hence, we propose the deep belief network (DBN) model, a deep learning type, used for the classification of the symptoms of fatigue in this study. DBN structure is a kind of neural network. The number of hidden layers within the network and the number of units in each hidden layer play important roles in the design of any neural network. Therefore, the hidden layer and the count of units in the DBN model designed in this paper have been selected as a result of various experiments. A greedy method has been adopted to adjust the structure of the deep belief network. Subsequently, the proposed DBN architecture test on KOU-DFD, YawDD and Nthu-DDD datasets. Comparative and experimental results concluded that the proposed DBN architecture is as robust as the other approaches found in the literature and achieves an accuracy rate of approximately 86%