YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series
<|reference_start|>YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series: This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. 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 General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.<|reference_end|>
@article{sapkota2024yolov10,
    title={YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You
  Only Look Once (YOLO) Series},
    author={Ranjan Sapkota, Rizwan Qureshi, Marco Flores Calero, Chetan Badjugar,
  Upesh Nepal, Alwin Poulose, Peter Zeno, Uday Bhanu Prakash Vaddevolu,
  Sheheryar Khan, Maged Shoman, Hong Yan, Manoj Karkee},
    journal={arXiv preprint arXiv:2406.19407},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.19407},
    primaryClass={cs.CV}
}