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Dergiler / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2026 / Volume: 68 Issue: 1

Comparative Evaluation of Yolo-Based Models for Multi-Class Weapon Detection

Selinay Erdinç(BAŞKENT ÜNİVERSİTESİ)0009-0002-5061-429X
Mehmet Dikmen(BASKENT UNIVERSITY)0000-0002-0584-5577
Sayfa
—
Yayın Tarihi
2026-06-30
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The experiments highlighted the potential of real-time AI-based object detectors for enhancing security systems.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:This study explores the effectiveness of various real-time object detectors for the problem of classifying different weapon types.
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Temel Bulgular & Veri:To that end, the performance of YOLOv9, YOLOv10, YOLOv11, YOLOX, and YOLOR models were evaluated on a balanced dataset of 15,387 images across 5 weapon classes.
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Sonuç & Katkı:The experiments highlighted the potential of real-time AI-based object detectors for enhancing security systems.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Object detection · weapon classification · YOLO-based models

Abstract

This study explores the effectiveness of various real-time object detectors for the problem of classifying different weapon types. To that end, the performance of YOLOv9, YOLOv10, YOLOv11, YOLOX, and YOLOR models were evaluated on a balanced dataset of 15,387 images across 5 weapon classes. All models are trained with 5-fold stratified cross-validation and YOLOR achieved the best overall performance with an [email protected] of 0.8654 and [email protected]:0.95 of 0.8380. In addition, YOLOR obtained a precision of 0.8550, recall of 0.9570, and F1-score of 0.9210. Results show YOLOR’s suitability for high-precision, critical applications, while YOLOX offers advantages for recall-focused, resource-constrained deployments with a recall of 0.9628 despite a lower precision of 0.6902. The experiments highlighted the potential of real-time AI-based object detectors for enhancing security systems.

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Bu Makaleye Atıf Yap

Erdinç, S., Dikmen, M. (2026). Comparative Evaluation of Yolo-Based Models for Multi-Class Weapon Detection. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Volume: 68 Issue: 1. https://doi.org/10.33769/aupse.1772978

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