Drowsiness Detection in the Advanced Driver-Assistance System using YOLO V5 Detection Model - Fauzan Ridho, Muhammad; Fransiskus Panca; Welly Yandi; Almeera Amsana Rachmani (Jurusan Teknik Elektro Fakultas Teknik Universitas Bangka Belitung, 2024)
The development of Artificial Intelligence (AI) in the automobile industry, particularly in the Advanced Driver-Assistance System (ADAS), has been increasing rapidly in recent years. One of the essential features of ADAS is the drowsiness alert system, which monitors the state of the driver's eyes. This article presents a study on the development of ADAS that focuses on drowsiness detection using deep learning through a Convolutional Neural Network (CNN) approach. The study utilized the YOLO V5 model for object detection, which was trained using custom datasets containing images annotated with two labels: