Implementation of Yolov8 to Detect Focus and Fatigue Levels of Employees
DOI:
https://doi.org/10.56480/jln.v5i3.1572
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Yolo, Fatigue Detection, Artificial Intelligence, Machine LearningAbstract
The coronavirus disease (Covid-19) pandemic that occurred at the end of 2019 had a major impact worldwide. In addition to impacting the health sector, other sectors such as business and technology are also very influential factors. At the same time, trends in artificial intelligence, machine learning, and deep learning are also emerging, bringing tremendous developments worldwide. New methods and advanced architectures such as YOLO (You Only Look Once) are attracting much attention because they can accurately detect objects with very high probability. YOLO is considered the "fastest deep learning object detector" and is an object detection network architecture that focuses on accuracy and speed. YOLOv8 allows you to perform accurate facial recognition to detect fatigue on employee faces. This detection evaluates how well employees focus on their work. To help companies maintain safety and prevent accidents at work. Such as jobs that require a level of focus to avoid accidents such as construction workers or taxi drivers or customers or people related to the job. The best level of accuracy obtained with YOLOv8 in real-time, has a detection accuracy or large mAP (Mean Average Precision) value of up to 0.977 or 97.7%.
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Copyright (c) 2025 Kevin Hanif Wicaksana, I Gede Susrama Mas Diyasa, Fetty Tri Anggraeny
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