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

arXiv: https://arxiv.org/abs/2501.13400 · HTML: https://mlsanity.com/paper/2501.13400/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information](https://mlsanity.com/paper/2402.13616/) (2024)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS](https://mlsanity.com/paper/2304.00501/) (2023)
- [YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection](https://mlsanity.com/paper/2308.05480/) (2023)
- [YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain](https://mlsanity.com/paper/2406.10139/) (2024)
- [YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems](https://mlsanity.com/paper/2408.09332/) (2024)

## Built on by (most cited first, 15 of 15)

- [ESCA: Contextualizing Embodied Agents via Scene-Graph Generation](https://mlsanity.com/paper/2510.15963/) (2025)
- [OmniHuman: A Large-scale Dataset and Benchmark for Human-Centric Video Generation](https://mlsanity.com/paper/2604.18326/) (2026)
- [TSBOW -- Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions](https://mlsanity.com/paper/2602.05414/) (2026)
- [YOLO26: A Comprehensive Architecture Overview and Key Improvements](https://mlsanity.com/paper/2602.14582/) (2026)
- [Enhanced Vehicle Speed Detection Considering Lane Recognition Using Drone Videos in California](https://mlsanity.com/paper/2506.11239/) (2025)
- [SynSpill: Improved Industrial Spill Detection With Synthetic Data](https://mlsanity.com/paper/2508.10171/) (2025)
- [Hardware-Aware YOLO Compression for Low-Power Edge AI on STM32U5 for Weeds Detection in Digital Agriculture](https://mlsanity.com/paper/2511.07990/) (2025)
- [SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented Gangue Detection](https://mlsanity.com/paper/2502.05988/) (2025)
- [YOLO-APD: Enhancing YOLOv8 for Robust Pedestrian Detection on Complex Road Geometries](https://mlsanity.com/paper/2507.05376/) (2025)
- [Comprehensive Benchmarking of YOLOv11 Architectures for Scalable and Granular Peripheral Blood Cell Detection](https://mlsanity.com/paper/2509.24595/) (2025)
- [LTV-YOLO: A Lightweight Thermal Object Detector for Young Pedestrians in Adverse Conditions](https://mlsanity.com/paper/2601.11662/) (2026)
- [Detection and Identification of Penguins Using Appearance and Motion Features](https://mlsanity.com/paper/2603.03603/) (2026)
- [Telescope: Learnable Hyperbolic Foveation for Ultra-Long-Range Object Detection](https://mlsanity.com/paper/2604.06332/) (2026)
- [CANSURF: An ASV-View Can Dataset and Benchmark for Detection and Tracking of Surface-Level Debris](https://mlsanity.com/paper/2605.16774/) (2026)
- [Infectious Bovine Pinkeye Detection Using Computer Vision and Imbalance-Aware Learning](https://mlsanity.com/paper/2609.23714/) (2026)

## Abstract (arXiv)

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.