Issue |
ITM Web Conf.
Volume 70, 2025
2024 2nd International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2024)
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Article Number | 03008 | |
Number of page(s) | 6 | |
Section | Image Processing and Computer Vision | |
DOI | https://doi.org/10.1051/itmconf/20257003008 | |
Published online | 23 January 2025 |
Comparative Analysis of YOLO Variants Based on Performance Evaluation for Object Detection
Guangdong Country Garden School, 523808 Guangdong, China
Corresponding author: miaokaihong@ldy.edu.rs
This study focuses on analysing and exploring the You Only Look Once (YOLO) algorithm. Specifically, this article analyses the evolution and performance of three versions (YOLOv1, YOLOv5, and YOLOv8) in object detection. The research begins by detailing the fundamental concepts of object detection and the datasets commonly used in this field. It then delves into the specific architectures and experimental outcomes associated with each YOLO version. The analysis reveals that while YOLOv8 introduces advanced features and improvements, earlier versions like YOLOv5 may offer superior stability and performance under certain conditions, particularly in specific tasks such as car detection. The discussion emphasizes the significant impact of factors such as batch size on model performance, suggesting that fine-tuning these parameters can optimize the algorithm for particular applications. The study concludes that the future of YOLO development lies in exploring and refining different variants, particularly those of YOLOv8, to better meet diverse requirements. By focusing on five distinct YOLOv8 variants, the research aims to enhance the adaptability and effectiveness of the YOLO framework across a wide range of object detection challenges, thereby contributing valuable insights into the ongoing advancement of this technology.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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