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 the field by achieving an optimal balance between speed and accuracy. The review traces the evolution of...
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MDPI AG
2024-12-01
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| Series: | Computers |
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| Online Access: | https://www.mdpi.com/2073-431X/13/12/336 |
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| author | Momina Liaqat Ali Zhou Zhang |
| author_facet | Momina Liaqat Ali Zhou Zhang |
| author_sort | Momina Liaqat Ali |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-81f1be0e27d148a3bbe0b33f8cbd8610 |
| institution | Kabale University |
| issn | 2073-431X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj-art-81f1be0e27d148a3bbe0b33f8cbd86102024-12-27T14:19:04ZengMDPI AGComputers2073-431X2024-12-01131233610.3390/computers13120336The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object DetectionMomina Liaqat Ali0Zhou Zhang1Department of Computer Science, Middle Tennessee State University, 1301 E Main St., Murfreesboro, TN 37132, USAFarmingdale State College, State University of New York, 2350 NY-110, Farmingdale, NY 11735, USAThis 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.https://www.mdpi.com/2073-431X/13/12/336YOLOsingle stage detectionYOLOv10YOLOv11performance evaluationdeep neural network |
| spellingShingle | Momina Liaqat Ali Zhou Zhang The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection Computers YOLO single stage detection YOLOv10 YOLOv11 performance evaluation deep neural network |
| title | The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection |
| title_full | The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection |
| title_fullStr | The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection |
| title_full_unstemmed | The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection |
| title_short | The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection |
| title_sort | yolo framework a comprehensive review of evolution applications and benchmarks in object detection |
| topic | YOLO single stage detection YOLOv10 YOLOv11 performance evaluation deep neural network |
| url | https://www.mdpi.com/2073-431X/13/12/336 |
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