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The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

282
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December 14, 2024
Published Date

Research Abstract & Technology Focus

This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between speed and accuracy. The review traces the evolution of YOLO variants, highlighting key architectural improvements, performance benchmarks, and applications in domains such as healthcare, autonomous vehicles, and robotics. It also evaluates the framework’s strengths and limitations in practical scenarios, addressing challenges like small object detection, environmental variability, and computational constraints. By synthesizing findings from recent research, this work identifies critical gaps in the literature and outlines future directions to enhance YOLO’s adaptability, robustness, and integration into emerging technologies. This review provides researchers and practitioners with valuable insights to drive innovation in object detection and related applications.
framework comprehensive review evolution applications benchmarks
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The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized th...
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What is the core focus of the research titled 'The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection'?

This literature focuses on: This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art model for object detection, YOLO has revolutionized the field by achieving an optimal balance between sp...

Are there open-source GitHub repositories related to The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection?

Yes, open-source projects like wanshuiyin/Auto-claude-code-research-in-sleep (ARIS ⚔️ (Auto-Research-In-Sleep) — Lightweight Markdown-only skills for autonomous ML research: cross-model review loops, idea discovery, and exper...) are actively building upon these concepts.

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Yes, highly correlated activity was mapped. An entry titled 'The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection' discusses this: This paper provides a comprehensive review of the YOLO (You Only Look Once) framework up to its latest version, YOLO 11. As a state-of-the-art mode...

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