[论文解读] YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review

Priyanto Hidayatullah, Nurjannah Syakrani+3 more|ArXiv.org|Jan 23, 2025

Building Energy and Comfort Optimization被引用 12

一句话总结

本文对 YOLOv8 到 YOLO11 进行了深入的架构比较，综合文献和源代码以阐明模型运作、比较演变并识别差距，最终版本已在 Jurnal RESTI 发表。

ABSTRACT

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 delve into 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.

研究动机与目标

• 提供对 YOLOv8–YOLO11 的全面、深入的架构比较，帮助读者理解每个模型如何运作以及它们有何不同。
• 综合来自相关论文、文档和源代码的信息，以映射各版本的架构变动。
• 突出四个最新 YOLO 模型中哪些模块保持不变以及在哪些方面进行了改进。
• 由于学术出版物有限、缺乏官方架构图，识别差距以指导未来工作。

提出的方法

• 系统性地评审与 YOLOv8–YOLO11 相关的学术论文、官方文档和源代码。
• 分析各版本的架构组件和特征提取模块，以识别改进和一致的模块。
• 将研究结果与可用的图解和公开资源进行交叉参考，以评估清晰度和差距。
• 讨论对未来开发者的影响，并倡导公开可用的架构资源。

实验结果

研究问题

• RQ1 YOLOv8、YOLOv9、YOLOv10 和 YOLO11 在架构上有哪些差异？
• RQ2 在这些 YOLO 世代中，哪些模块保持不变？
• RQ3 在 YOLOv8–YOLO11 的架构和特征提取方面观察到了哪些改进？
• RQ4 由于出版物和官方架构图的受限存在哪些差距，以及它们如何影响理解和未来工作？

主要发现

• 每个 YOLO 版本在架构和特征提取方面相较前一代有改进。
• 某些架构模块在 YOLOv8 到 YOLO11 之间保持不变。
• 学术出版物和官方架构图的缺乏妨碍了对模型功能的全面理解。
• 本研究强调需要公开可访问的图解和文档以帮助未来开发。）