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Mühendislik Bilimleri ve Araştırmaları Dergisi

2687-4415

Bandırma Onyedi Eylül Üniversitesi

10.46387/bjesr.1739026

Semi- and Unsupervised Learning

Yarı ve Denetimsiz Öğrenme

Küçük Boyutlu İHA Tespiti için Ankraj Tabanlı YOLOv11 ve Ankrajsız FCOS’un Karşılaştırmalı Değerlendirmesi

Comparative Evaluation of Anchor-Based YOLOv11 and Anchor-Free FCOS for Small-Object UAV Detection

https://orcid.org/0009-0008-2922-0580

Yücel

Hilal İkra

BANDIRMA ONYEDI EYLUL UNIVERSITY

https://orcid.org/0000-0003-2112-5497

Özer

İlyas

BANDIRMA ONYEDI EYLUL UNIVERSITY

https://orcid.org/0000-0002-9940-0471

Dalcalı

Adem

BANDIRMA ONYEDI EYLUL UNIVERSITY

10 27 2025

7 2 214 221 07 09 2025 08 07 2025

2019

Mühendislik Bilimleri ve Araştırmaları Dergisi

Bu çalışmada, kritik altyapı ve kentsel alanlarda insansız hava araçlarının (İHA) düşük maliyetli görüntü tabanlı tespiti incelenmiştir. Histogram of Oriented Gradients (HOG), Scale-Invariant Feature Transform (SIFT) ve Local Binary Patterns (LBP) gibi geleneksel el işi özellik yöntemleri ile Support Vector Machines (SVM) ve Adaptive Boosting (AdaBoost) gibi tek aşamalı sınıflandırıcıların dinamik koşullarda küçük hedefleri tanımada yetersiz kaldığı gösterilmiştir. Bu sorunu aşmak amacıyla, “UAV Drone” Kaggle veri seti üzerinde çapa tabanlı YOLOv11 ve ankrajsız FCOS mimarileri karşılaştırılmıştır. Her iki model de 640×640 boyutlandırma, normalizasyon ve veri artırma içeren birleşik bir ön işleme hattı ve AdamW optimizatörü ile OneCycleLR öğrenme planı kullanılarak üç katlı çapraz doğrulama ile 50 epoch boyunca eğitilmiştir. Sonuçlar, YOLOv11’in yaklaşık 10 FPS hızda %66,6 mAP@[0.5–0.95], FCOS’un ise yaklaşık 20 FPS hızda %64,1 mAP ve daha düşük bellek kullanımı sağladığını ortaya koymuştur. Bu nedenle, yüksek doğruluk gerektiren araştırmalar için YOLOv11, gerçek zamanlı ve kaynak kısıtlı uygulamalar için ise FCOS türevleri önerilmektedir.

In this study, low-cost image-based detection of Unmanned Aerial Vehicle (UAVs) over critical infrastructure and urban areas is investigated. Traditional hand-crafted feature methods (Histogram of Oriented Gradients (HOG), Scale-Invariant Feature Transform (SIFT), Local Binary Patterns (LBP)) and single-stage classifiers (Support Vector Machines (SVM), Adaptive Boosting (AdaBoost)) are shown to struggle with small targets under dynamic conditions. To address this, anchor-based YOLOv11 and anchor-free Fully Convolutional One-Stage (FCOS) architectures are compared on the “UAV Drone” Kaggle dataset. Both models use a unified preprocessing pipeline (resize to 640×640, normalization, data augmentation) and three-fold cross-validation for 50 epochs with the AdamW optimizer and a OneCycleLR schedule. Results reveal that YOLOv11 achieves 66.6 % mAP@[0.5–0.95] at ~10 FPS, while FCOS attains 64.1 % mAP at ~20 FPS with lower memory use. Thus, YOLOv11 is recommended for high-accuracy research, and FCOS variants for real-time, resource-constrained applications.

UAV Detection Anchor-Based Object Detection Fully Convolutional One-Stage.

İHA Tespiti Ankraj Tabanlı Nesne Tespiti Tam Konvolüsyonel Tek Aşamalı

This work was supported by the Bandırma Onyedi Eylül University Scientific Research Projects Coordination Unitunder Project BAP-23-1004-008.

BAP-23-1004-008

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