计算机科学
建筑
人工智能
目标检测
机器人学
系统工程
人机交互
工程类
机器人
地理
考古
模式识别(心理学)
作者
Juan Terven,Diana-Margarita Córdova-Esparza,Julio-Alejandro Romero-González
摘要
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 up to YOLOv8, YOLO-NAS, and YOLO with transformers. 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.
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