Abstract
Driving is one of the common activities in people’s everyday life and therefore improving driving skill to reduce car crashes is an important issue. Even though a lot of studies and work has been done on road and vehicle designs to improve driver’s safety yet the total number of car crashes is increasing day by day. Therefore, the most factors that cause an accident is fatigue driver rather than other factors which are distraction, speeding, drinking driver, drugs and depression. To prevent car crashes that occur due to drowsy driver, it is essential to have an assistive system that monitors the vigilance level of driver and alert the driver in case of drowsy detection. This system presents a drowsy detection system based on eye detection of the driver. Vision-based approach is adopted to detect drowsy eye because other developed approaches are either intrusive (physical approach) that makes the driver uncomfortable or less sensitive (vehicle based approach). The data collected from 26 volunteers will have four (4) different type of image. Thus, the total input will be 10,800 nodes. This thesis will be classified into two (2) outputs which are drowsy eye and non-drowsy eye. The algorithm that will be used is Back-propagation Neural Network (BPNN) and will be applied in MATLAB software. The experimental result shows that this system could achieve 98.1% accuracy.
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Shamsuddin, M.R.B., Sahar, N.N.B.S., Rahmat, M.H.B. (2017). Eye Detection for Drowsy Driver Using Artificial Neural Network. In: Mohamed, A., Berry, M., Yap, B. (eds) Soft Computing in Data Science. SCDS 2017. Communications in Computer and Information Science, vol 788. Springer, Singapore. https://doi.org/10.1007/978-981-10-7242-0_10
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DOI: https://doi.org/10.1007/978-981-10-7242-0_10
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