Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Refdinal Tubagus
Stunning Vision AI
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
YOLOv8 to YOLO11 Performance Benchmark and Comprehensive Architectural Comparative Review Priyanto Hidayatullah; Nurjannah Syakrani; Muhammad Rizqi Sholahuddin; Trisna Gelar; Refdinal Tubagus
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 2 (2026): April - In progress
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i2.6598

Abstract

In the domain of deep learning-driven computer vision, YOLO is revolutionary. However, not all YOLO models are accompanied by academic articles and architectural diagrams. It complicates the comprehension of the model's operation. Moreover, the existing review papers fail to examine each model comprehensively. This work aims to provide a thorough comparative analysis of the architectures from YOLOv8 to YOLO11, allowing readers to swiftly understand the operational mechanisms and differences among the models. We analyzed the architecture of each YOLO version by reviewing relevant scholarly articles, official documentation, and examining the source code. In particular, we discovered that YOLOv8 through YOLO11 differ in novelty while sharing similarities in the anchor-free and Non-Maximum Suppression (NMS) aspects, except YOLOv10 (NMS-free). Each also has drawbacks, such as differing levels of complexity in the way features are connected (v8), architectural structure and training (v9), training methods or dual assignments (v10), inference, and code implementation (v11). While each version improves architecture, some blocks remain unchanged. This study helps readers understand different YOLO version architectures and inspires how to improve their performance. It also provides readers with a comprehensive architecture diagram and detailed descriptions of each block, serving as a reference for both academic and practical applications. In terms of performance, a benchmark using the Roboflow 100 dataset reveals that YOLOv9 achieves superior accuracy; however, it is eight times slower owing to its NMS mechanism. YOLOv10 is the fastest but least accurate, whereas YOLOv8 and YOLO11 provide a balanced compromise between speed and accuracy.
Co-Authors Gelar, Trisna Muhammad Rizqi Sholahuddin Nurjannah Syakrani Priyanto Hidayatullah