Ceviz işleme hatları için YOLOv11 model temelli otomatik tespit ve sınıflandırma sistemleri

Year 2025, Volume: 14 Issue: 4, 1638 - 1646, 15.10.2025

Hakan Aktaş , Emrullah Polat

Abstract

Tarım ürünlerinin otomatik sınıflandırılması, kalite kontrol süreçlerinin hızlandırılması ve insan hatasının azaltılması açısından kritik öneme sahiptir. Bu çalışmada konveyör bant üzerinde bulunan çoklu ceviz bileşenlerinin gerçek zamanlı olarak tespiti ve sınıflandırılması amacıyla, YOLOv11 tabanlı bir derin öğrenme yöntemi önerilmiştir. Endüstriyel bir düzenek üzerinde görüntüler alınmış ve toplamda 1194 adet kabuk, 641 adet ceviz içi ve 458 adet zar görüntüsü etiketlenmiştir. Etiketlenen bu veriler ilk önce YOLOv11n modeli ile eğitilip test edilmiş olup; en yüksek recall değeri ceviz içi sınıfında 0.963 olarak ve en düşük recall değeri zar sınıfında 0.795 olarak hesaplanmıştır. Aynı etiketli veri seti daha büyük bir model olan YOLOv11L modeli ile eğitilip test edildiğinde en yüksek recall değeri ceviz içi sınıfında 0.977 ve en düşük recall değeri zar sınıfında 0.922 olarak hesaplanmıştır. Son olarak YOLO modellerinden farklı olarak RT-DETR nesne tespit algoritması alternatif bir model olarak kullanılmış ve sonuçları YOLOv11 modelleri ile kıyaslanmıştır. Her bir modelin kendine göre üstün yanları olup; elde edilen test sonuçlarına göre yüksek fps uygulamaları için YOLOv11n modeli, orta fps ve yüksek doğruluk için YOLOv11L modeli, orta - düşük fps değerlerinde kabuk ve ceviz içi sınıflarının yüksek doğrulukta tespiti için RT-DETR modeli önerilmektedir.

Keywords

Nesne Tespiti , Nesne Sınıflandırma , YOLOv11 , RT-DETR , Ceviz Tespit ve Sınıflandırma

Automatic detection and classification systems based on YOLOv11 model for walnut processing lines

Abstract

Automatic classification of agricultural products is critical for accelerating quality control processes and reducing human error. In this study, a YOLOv11-based deep learning method is proposed for real-time detection and classification of multiple walnut components on a conveyor belt. Images were captured in an industrial setup, and a total of 1194 shell, 641 kernel, and 458 membrane images were labeled. These labeled data were first trained and tested with the YOLOv11n model; the highest recall value was calculated as 0.963 in the walnut class and the lowest recall value was calculated as 0.795 in the membrane class. When the same labeled dataset was trained and tested with the larger YOLOv11L model, the highest recall value was 0.977 for the walnut kernel class, and the lowest recall value was 0.922 for the membrane class. Finally, unlike the YOLO models, the RT-DETR object detection algorithm was employed as an alternative model, and its results were compared with those of the YOLOv11 models. Each model has its own advantages; based on the test results, the YOLOv11n model is recommended for high fps applications, the YOLOv11L model for medium fps with high accuracy, and the RT-DETR model is recommended for high accuracy detection of shell and walnut kernel classes at medium - low fps values.

Keywords

Object Detection , Object Classification , YOLOv11n , RT-DETR , Walnut Detection and Classification

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