{
  "id": "2304.00501",
  "version": "v1",
  "published": "2023-04-02T10:27:34.000Z",
  "updated": "2023-04-02T10:27:34.000Z",
  "title": "A Comprehensive Review of YOLO: From YOLOv1 to YOLOv8 and Beyond",
  "authors": [
    "Juan Terven",
    "Diana Cordova-Esparza"
  ],
  "comment": "27 pages, 12 figures, 4 tables, submitted to ACM Computing Surveys",
  "categories": [
    "cs.CV"
  ],
  "abstract": "YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO to YOLOv8. We start by describing the standard metrics and postprocessing; then, we discuss the major changes in network architecture and training tricks for each model. Finally, we summarize the essential lessons from YOLO's development and provide a perspective on its future, highlighting potential research directions to enhance real-time object detection systems.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2023-04-02T10:27:34.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "comprehensive review",
      "enhance real-time object detection systems",
      "central real-time object detection system",
      "highlighting potential research directions",
      "essential lessons"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 27,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}