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Journals / Tasarım Mimarlık ve Mühendislik Dergisi / 2026 / Sayı: 1

VERY LARGE SCALE CROSS CORPUS OBJECT DETECTION APPLICATIONS WITH THE LATEST YOLO MODELS ON SINGLE OBJECT LASOT DATASET

YOLO modelleriyle Tek Nesne Lasot Verisetinde Geniş Ölçekli Çapraz Nesne Tanıma Uygulamaları

Cevahir Parlak(FENERBAHÇE ÜNİVERSİTESİ)0000-0002-5500-7379
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Publication Date
2026-07-22
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AI Summary & Key Takeaways

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

Results of the experiments show that YOLOv9e is the best model in terms of accuracy metrics by 0.3859 [email protected] and 0.6490 recall, however, YOLOv10X is the fastest YOLO model with 201.18 fps.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, we explore the latest YOLO models, namely, YOLOv8X, YOLOv9e, YOLOv10X, YOLO11X, and YOLO12X in the single object detection task with LaSOT dataset.
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Key Findings & Data:YOLO is a great breakthrough in object detection and fills in the gap between high performance and accuracy in real time applications.
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Conclusion & Impact:Results of the experiments show that YOLOv9e is the best model in terms of accuracy metrics by 0.3859 [email protected] and 0.6490 recall, however, YOLOv10X is the fastest YOLO model with 201.18 fps.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Nesne Tanıma · YOLO · Derin Öğrenme

Abstract

In this study, we explore the latest YOLO models, namely, YOLOv8X, YOLOv9e, YOLOv10X, YOLO11X, and YOLO12X in the single object detection task with LaSOT dataset. YOLO is a great breakthrough in object detection and fills in the gap between high performance and accuracy in real time applications. YOLO tries to maintain accuracy with a reasonable speed which makes it very suitable for real time object recognition applications. This study evaluates the latest YOLO models in terms of accuracy and speed in a very large-scale object detection task on the single object LaSOT dataset. All YOLO models used in this study are pretrained on the COCO dataset and evaluated on the LaSOT dataset. COCO and LaSOT datasets have 30 common classes which include more than 1.5 million image samples. Cross corpus experiments are very formidable challenges and ultimate test for the generalizability and integrity of machine learning models where the models are trained in a dataset and tested in another dataset. Results of the experiments show that YOLOv9e is the best model in terms of accuracy metrics by 0.3859 [email protected] and 0.6490 recall, however, YOLOv10X is the fastest YOLO model with 201.18 fps.

Özet

Bu çalışmada, LaSOT veri kümesi ile tekli nesne tespitinde en son YOLO modellerini, YOLOv8X, YOLOv9e, YOLOv10X, YOLO11X ve YOLO12X modelleri incelenmektedir. YOLO, nesne tespitinde büyük bir çığır açmıştır ve gerçek zamanlı uygulamalarda yüksek performans ile doğruluk arasındaki boşluğu doldurmaktadır. YOLO, doğruluğu makul bir hızla korumaya çalışır; bu da onu gerçek zamanlı nesne tanıma uygulamaları için oldukça uygun hale getirir. Bu çalışma, en son YOLO modellerini, tekli nesne içeren LaSOT veri kümesi üzerindeki çok büyük ölçekli bir nesne tespiti görevinde doğruluk ve hız açısından değerlendirmektedir. Çalışmada kullanılan tüm YOLO modelleri, COCO veri kümesi üzerinde önceden eğitilmiş ve LaSOT veri kümesi üzerinde değerlendirilmiştir. COCO ve LaSOT veri kümeleri, 1,5 milyondan fazla görüntü örneğini barındıran 30 ortak sınıfa sahiptir. Modellerin bir veri kümesinde eğitilip başka bir veri kümesinde test edildiği çapraz-veriseti deneyleri, makine öğrenimi modellerinin genellenebilirliği ve bütünlüğü için oldukça çetin zorluklar teşkil eder ve nihai bir test niteliğindedir. Deney sonuçları, YOLOv9e'nin 0.3859 [email protected] ve 0.6490 duyarlılık (recall) değerleri ile doğruluk metrikleri açısından en iyi model olduğunu, buna karşın YOLOv10X'in 201.18 fps ile en hızlı YOLO modeli olduğunu göstermektedir.

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Cite This Article

Parlak, C. (2026). VERY LARGE SCALE CROSS CORPUS OBJECT DETECTION APPLICATIONS WITH THE LATEST YOLO MODELS ON SINGLE OBJECT LASOT DATASET. Tasarım Mimarlık ve Mühendislik Dergisi, Sayı: 1. https://doi.org/10.59732/dae.1827622

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