Drowsy Detection in the Eye Area using the Convolutional Neural Network

Authors

  • Alessandro Benito Putra Bayu Wedha Bina Nusantara (BINUS ASO), Indonesia
  • Ben Rahman Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Indonesia
  • Djarot Hindarto Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Indonesia https://orcid.org/0000-0001-7501-2610
  • Bayu Yasa Wedha Fakultas Teknologi Komunikasi dan Informatika, Universitas Nasional, Indonesia

DOI:

10.33395/sinkron.v8i2.12386

Keywords:

Convolutional Neural Network, Close Eyes, Detection Drowsy, Fatigue, Prototype

Abstract

Detection of a drowsy driver is an important aspect of driving safety. For this reason, it is necessary to have technology to carry out early detection before fatigue occurs. Mainly focused on driver fatigue that occurs at night. Analysis can be done quickly and accurately. These conditions can be sent via data so that they can be monitored and analyzed in real time. The results of the analysis can be sent by communication via the internet network. In addition, it functions as an early warning and can be used as logging or records that can be stored. This research does not discuss data communication but makes a prototype for detecting sleepy drivers. Prototype created using the Convolutional Neural Network Algorithm. The detection area is in the eye and testing is carried out with the brightness level of the light. In this study, building a prototype to detect signs of driver fatigue using the Convolutional Neural Network algorithm. The detection area used is in the eye, by testing at different light brightness levels. The dataset used in this study consists of a series of eye images, which are divided into two classes, namely open eyes, and closed eyes. After conducting the training process on Convolutional Neural Network, we get results of detection accuracy reaching 90%.

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How to Cite

Wedha, A. B. P. B. ., Rahman, B. ., Hindarto, D., & Wedha, B. Y. . (2023). Drowsy Detection in the Eye Area using the Convolutional Neural Network. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(2), 1097-1107. https://doi.org/10.33395/sinkron.v8i2.12386

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