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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 17

Advancing mask detection with attention-driven and bayesian-optimized ensemble models

Dikkat mekanizmalı ve bayesyan optimizasyonlu topluluk öğrenme modelleri ile maske tespiti

Musa Balcı(KIRIKKALE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ PR.)0009-0001-8539-8083
Andaç Fındıkçı(KIRIKKALE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ PR.)0009-0006-4789-6029
Mustafa Yasin Erten(KIRIKKALE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-5140-1213
Hüseyin Aydilek(KIRIKKALE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ PR.)0000-0003-3051-4259
Pages
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Publication Date
2026-05-05
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The Bayes Search-optimized model demonstrated superior mask detection performance compared to other methods in the literature.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Infectious diseases have caused significant losses throughout human history and are increasingly prevalent today due to population growth.
📊
Key Findings & Data:Pandemics such as COVID-19 and monkeypox have rapidly spread through human interaction-driven transmission pathways.
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Conclusion & Impact:The Bayes Search-optimized model demonstrated superior mask detection performance compared to other methods in the literature.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Derin öğrenme · Salgın hastalıklar · Maske tespiti

Abstract

Infectious diseases have caused significant losses throughout human history and are increasingly prevalent today due to population growth. Pandemics such as COVID-19 and monkeypox have rapidly spread through human interaction-driven transmission pathways. Although mask usage is an effective method to reduce transmission, its inadequate implementation necessitates monitoring. This study aims to prevent the spread of pandemics by detecting mask usage through artificial intelligence and deep learning algorithms. Models were trained on images of masked, unmasked, and improperly masked individuals using Resnet101, MobileNet, and Xception algorithms, and three models were developed: Mask Ensemble, Attention Mask Ensemble, and Bayes Mask Ensemble. Hyperparameter tuning was performed using Bayes Search optimization, revealing that the Bayes Mask Ensemble model achieved the highest performance, followed by the Attention Mask Ensemble model, with the Mask Ensemble model ranking third. The Bayes Search-optimized model demonstrated superior mask detection performance compared to other methods in the literature.

Özet

Salgın hastalıklar, insanlık tarihinde büyük kayıplara neden olmuş ve günümüzde nüfus artışı nedeniyle daha sık görülmektedir. COVID-19 ve maymun çiçeği gibi salgınlar, insan etkileşiminden kaynaklanan bulaşma yollarıyla hızla yayılmaktadır. Maske kullanımı bulaşmayı azaltan etkili bir yöntem olmasına rağmen, yetersiz uygulanması denetim ihtiyacını doğurmaktadır. Bu çalışma, yapay zeka ve derin öğrenme algoritmalarıyla maske kullanımı tespitini sağlayarak salgınların yayılmasını önlemeyi amaçlamaktadır. Resnet101, MobileNet ve Xception algoritmaları kullanılarak maskeli, maskesiz ve yanlış takılan maske görselleri üzerinde modeller eğitilmiş; Mask Ensemble, Attention Mask Ensemble ve Bayes Mask Ensemble modelleri geliştirilmiştir. Bayes Search optimizasyonuyla hiperparametre ayarları incelenmiş, Bayes Mask Ensemble modelinin en yüksek performansı gösterdiği, bunu Attention Mask Ensemble modelinin takip ettiği ve Mask Ensemble modelinin üçüncü sırada yer aldığı bulunmuştur. Bayes Search ile optimize edilmiş model, literatürdeki diğer yöntemlere göre üstün maske tespiti sağlamıştır.

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Cite This Article

Balcı, M., Fındıkçı, A., Erten, M. Y., Aydilek, H. (2026). Advancing mask detection with attention-driven and bayesian-optimized ensemble models. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 17. https://doi.org/10.28948/ngumuh.1767952

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