YOLO vs. CNN Algorithms: A Comparative Study in Masked Face Recognition

Muhammad Ridho Dewanto (Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia)
Mifta Nur Farid (Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia)
Muhammad Abby Rafdi Syah (Department of Electrical Engineering, Institut Teknologi Kalimantan, Balikpapan, Indonesia)
Aji Akbar Firdaus (Department of Engineering, Universitas Airlangga, Surabaya, Indonesia)
Hamzah Arof (Department of Electrical Engineering, University of Malaya, Malaysia)

Scientific Journal of Informatics
Vol 11, No 1 (2024): February 2024

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.