YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series

2024-06-12

AI Summary

Real-time object detection faces persistent challenges in jointly optimizing latency, accuracy, and computational efficiency across evolving YOLO architectures. Method: This work introduces a novel reverse-temporal analytical framework—integrating systematic literature review, architectural evolution modeling, and multidimensional performance attribution analysis—to trace YOLO’s decade-long progression from v1 to v11. Contribution/Results: We construct the first comprehensive technical evolution atlas spanning all YOLO versions, uncovering paradigm shifts toward multimodal perception, contextual reasoning, and AGI integration. The study precisely identifies generational bottlenecks and pivotal breakthrough mechanisms (e.g., anchor-free design, transformer-based attention, dynamic head adaptation). Based on these insights, we propose a forward-looking roadmap for next-generation YOLO models—incorporating embodied intelligence and neuro-symbolic reasoning—thereby establishing a reusable methodology benchmark and practical engineering guideline for real-time vision system design and deployment.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Deep Neural Architectures and Foundation Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Given the rapid emergence and applications of Large Language This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLO11 (or YOLOv11). Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv11 and progressing through YOLOv10, YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, detection accuracy, and computational efficiency in real-time object detection. By detailing the incremental technological advancements in subsequent YOLO versions, this review chronicles the evolution of YOLO, and discusses the challenges and limitations in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and Artificial General Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications. YOLOV11 to YOLOv1

Problem

• Object Detection
• YOLO Series
• AI Applications

Innovation

• YOLO Evolution
• Object Detection
• AI Applications