# YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLO11, and Their Previous Versions

arXiv: https://arxiv.org/abs/2411.00201 · HTML: https://mlsanity.com/paper/2411.00201/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 85 references that are in the map.
- First posted on arXiv 31 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [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)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications](https://mlsanity.com/paper/2209.02976/) (2022)
- [A Review and Comparative Study on Probabilistic Object Detection in Autonomous Driving](https://mlsanity.com/paper/2011.10671/) (2020)
- [Enhancing Traffic Safety with Parallel Dense Video Captioning for End-to-End Event Analysis](https://mlsanity.com/paper/2404.08229/) (2024)
- [YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems](https://mlsanity.com/paper/2410.22898/) (2024)
- [Road Scenes Segmentation Across Different Domains by Disentangling Latent Representations](https://mlsanity.com/paper/2108.03021/) (2021)
- [BenchCloudVision: A Benchmark Analysis of Deep Learning Approaches for Cloud Detection and Segmentation in Remote Sensing Imagery](https://mlsanity.com/paper/2402.13918/) (2024)

## Built on by (most cited first, 18 of 18)

- [PyPotteryLens: An Open-Source Deep Learning Framework for Automated Digitisation of Archaeological Pottery Documentation](https://mlsanity.com/paper/2412.11574/) (2024)
- [YolovN-CBi: A Lightweight and Efficient Architecture for Real-Time Detection of Small UAVs](https://mlsanity.com/paper/2512.18046/) (2025)
- [A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation](https://mlsanity.com/paper/2503.01605/) (2025)
- [Enhanced Vehicle Speed Detection Considering Lane Recognition Using Drone Videos in California](https://mlsanity.com/paper/2506.11239/) (2025)
- [Container damage detection using advanced computer vision model Yolov12 vs Yolov11 vs RF-DETR A comparative analysis](https://mlsanity.com/paper/2506.22517/) (2025)
- [A Lightweight and Robust Framework for Real-Time Colorectal Polyp Detection Using LOF-Based Preprocessing and YOLO-v11n](https://mlsanity.com/paper/2507.10864/) (2025)
- [A Comparative Evaluation of Large Vision-Language Models for 2D Object Detection under SOTIF Conditions](https://mlsanity.com/paper/2601.22830/) (2026)
- [Learning on the Fly: Replay-Based Continual Object Perception for Indoor Drones](https://mlsanity.com/paper/2602.13440/) (2026)
- [Robust and Label-Efficient Deep Waste Detection](https://mlsanity.com/paper/2508.18799/) (2025)
- [Re-Identifying K=ak=a with AI-Automated Video Key Frame Extraction](https://mlsanity.com/paper/2510.08775/) (2025)
- [Big Data, Tiny Targets: An Exploratory Study in Machine Learning-enhanced Detection of Microplastic from Filters](https://mlsanity.com/paper/2510.18089/) (2025)
- [Promptable Fire Segmentation: Unleashing SAM2's Potential for Real-Time Mobile Deployment with Strategic Bounding Box Guidance](https://mlsanity.com/paper/2510.21782/) (2025)
- [A Hierarchical Computer Vision Pipeline for Physiological Data Extraction from Bedside Monitors](https://mlsanity.com/paper/2511.23355/) (2025)
- [Plug to Place: Indoor Multimedia Geolocation from Electrical Sockets for Digital Investigation](https://mlsanity.com/paper/2512.16620/) (2025)
- [Enhancing Vision Language Models with Logic Reasoning for Situational Awareness](https://mlsanity.com/paper/2601.11322/) (2026)
- [Automated multi-class wound assessment using dedicated instance segmentation models for boundary detection and classification](https://mlsanity.com/paper/2603.27325/) (2026)
- [Integrating Local Detail and Global Context: A Dual-Input Multi-Task Learning Framework for Bone Tumor Diagnosis](https://mlsanity.com/paper/2609.28732/) (2026)
- [Beyond Masks and Trajectories: Flow-Guided Latent Action Injection for Stable Surgical Video Generation](https://mlsanity.com/paper/2610.09800/) (2026)

## Abstract (arXiv)

This study presents a comprehensive benchmark analysis of various YOLO (You Only Look Once) algorithms. It represents the first comprehensive experimental evaluation of YOLOv3 to the latest version, YOLOv12, on various object detection challenges. The challenges considered include varying object sizes, diverse aspect ratios, and small-sized objects of a single class, ensuring a comprehensive assessment across datasets with distinct challenges. To ensure a robust evaluation, we employ a comprehensive set of metrics, including Precision, Recall, Mean Average Precision (mAP), Processing Time, GFLOPs count, and Model Size. Our analysis highlights the distinctive strengths and limitations of each YOLO version. For example: YOLOv9 demonstrates substantial accuracy but struggles with detecting small objects and efficiency whereas YOLOv10 exhibits relatively lower accuracy due to architectural choices that affect its performance in overlapping object detection but excels in speed and efficiency. Additionally, the YOLO11 family consistently shows superior performance maintaining a remarkable balance of accuracy and efficiency. However, YOLOv12 delivered underwhelming results, with its complex architecture introducing computational overhead without significant performance gains. These results provide critical insights for both industry and academia, facilitating the selection of the most suitable YOLO algorithm for diverse applications and guiding future enhancements.