YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition
(1) Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia
(2) Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia
(3) Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia
(4) Department of Engineering, Universitas Airlangga, Surabaya, Indonesia
(5) Department of Electrical Engineering, University of Malaya, Malaysia
Abstract
Purpose: This research investigates the effectiveness of YOLO (You Only Look Once) and Convolutional Neural Network (CNN) in real-time face mask recognition, addressing the challenges posed by mask-wearing in infectious disease prevention.
Method: Utilizing a diverse dataset and employing YOLO's object detection and a combined Haar Cascade Algorithm with CNN, the study evaluated key performance indicators, including accuracy, framerate, and F1 Score.
Results: Results indicated that CNN outperformed YOLO in accuracy (99.3% vs. 79.3%) but operated at a slightly lower framerate. YOLO excelled in recall and precision, presenting a compelling choice for specific application needs. The research underscores the importance of considering factors beyond accuracy for informed decision-making in the realm of face mask recognition.
Novelty: This research evaluates the real-time performance of YOLO and CNN algorithms in masked face recognition, highlighting the crucial balance between framerate efficiency and detection accuracy.Keywords
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