Comparative Performance Analysis Method of Yolov9 and Yolov10 Models with Various Objects

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Bilişim Teknolojileri Dergisi
Cilt: 18 Sayı: 4
Comparative Performance Analysis Method of Yolov9 and Yolov10 Models with Various Objects

Comparative Performance Analysis Method of Yolov9 and Yolov10 Models with Various Objects

Pages : 297-303

View : 180 | Download : 425

Publication Date : 2025-10-31

Article Type : Research Paper

Abstract :Comparative Performance Analysis Method of Yolov9 and Yolov10 Models with Various Objects Araştırma Makalesi/Research Article Mert DEMİR Ege Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği, İzmir, Türkiye [email protected] (Geliş/Received:21.01.2025; Kabul/Accepted:19.09.2025) DOI: 10.17671/gazibtd.1624632 Abstract— Object detection applications are the cornerstone of computer vision studies, and one of them is YOLO. The YOLO model has recently attracted attention with its various versions as a popular fast object detection method. Each YOLO model released has aroused users\\\' curiosity about performance and data processing success. Different versions of this model are compared by performing a detection study on a single object. However, evaluating a model based on a single object is nothing more than a limited observation study. Improvements are seen in object detection score in YOLOv10. Many studies say that the latest model YOLOv10 has better object recognition success than YOLOv9, but a comprehensive object detection comparison has shown that YOLOv9 is better than the latest model YOLOv10 in object detection success. Object recognition models are classically analyzed on a single object image. This is a limited test of success. In this study, unlike classical approaches, images of 20 different objects belonging to each object group and 50 test images are presented to observe the performance success, and the working criteria of both models are presented comparatively. As a result of 40 experiments, with the proposed average object recognition criterion method, success rates of up to 72.7% for YOLOv9 and 64.9% for YOLOv10 were achieved, and the error margins for these models were calculated as 27.3% and 35.1%.
Keywords : YOLOv10, YOLOv9, performans karşılaştırması, derin öğrenme, nesne algılama

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