YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review

TL;DR

This paper provides a detailed comparative review of YOLOv8 to YOLO11, analyzing their architectures to clarify differences and improvements, addressing gaps in official documentation and scholarly publications.

Contribution

It offers an in-depth architectural comparison of the latest YOLO models, based on analysis of academic papers, documentation, and source code, highlighting key similarities and differences.

Findings

Each YOLO version shows architectural improvements and feature extraction enhancements.

Some blocks in the models remain unchanged across versions.

Lack of official diagrams complicates understanding and future development.

Abstract

Note: This is a preliminary version of the manuscript. The final, peer-reviewed, and substantially revised version has been published in Jurnal RESTI. Readers are encouraged to access and cite the published version: DOI: https://doi.org/10.29207/resti.v10i2.6598 In the field of deep learning-based computer vision, YOLO is revolutionary. With respect to deep learning models, YOLO is also the one that is evolving the most rapidly. Unfortunately, not every YOLO model possesses scholarly publications. Moreover, there exists a YOLO model that lacks a publicly accessible official architectural diagram. Naturally, this engenders challenges, such as complicating the understanding of how the model operates in practice. Furthermore, the review articles that are presently available do not investigate the specifics of each model. The objective of this study is to present a comprehensive and in-depth architecture comparison of the four most recent YOLO models, specifically YOLOv8 through YOLO11, thereby enabling readers to quickly grasp not only how each model functions, but also the distinctions between them. To analyze each YOLO version's architecture, we meticulously examined the relevant academic papers, documentation, and scrutinized the source code. The analysis reveals that while each version of YOLO has improvements in architecture and feature extraction, certain blocks remain unchanged. The lack of scholarly publications and official diagrams presents challenges for understanding the model's functionality and future enhancement. Future developers are encouraged to provide these resources.