Analisis Sistem Deteksi Kantuk Berbasis Computer Vision

Enggar, Yuwanda Putra (2022) Analisis Sistem Deteksi Kantuk Berbasis Computer Vision. Undergraduate Thesis thesis, Institut Teknologi Telkom Purwokerto.

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Abstract

The number of accidents that occur is not only caused by the lack of a vehicle, the human factor or human error turns out to play the biggest role in the occurrence of accidents. The condition of the rider's body is an important factor in determining the level of rider safety. With a good body condition, a driver will be able to drive a vehicle so that it can arrive at its destination safely. This study aims to test the accuracy of object detection with three conditions, namely normally, tired and sleepy using the MediaPipe framework, OpenCV model architecture, detection of sleepiness in humans is tested through a camera device on the dashboard in real time to assess the performance of the detection model. The test results show that the average accuracy of detecting sleepiness reaches 90%, this indicates that the detection model works quite well on the dashboard device. Keywords: Drowsiness Detection, MediaPipe Framework, OpenCV, Accuracy

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