Technical Architecture of YOLO: A Review

摘要

In the fast-changing world of computer vision, the You Only Look Once (YOLO) design has become an important development, changing object detection with its special structure. First introduced in 2016, YOLO stands out by combining the detection task into one neural network, allowing for quick processing and impressive accuracy. This essay looks closely at the technical details of YOLO, studying its main ideas, design improvements, and performance data through different versions, from YOLOv1 to the latest YOLOv7. By explaining the strengths and possible downsides of the algorithms, this review seeks to offer a clear picture of YOLO's effect on both academic studies and practical uses in various areas, like self-driving cars, security systems, and robotics. In the end, the study shows that YOLO not only improves object detection methods but also sets the stage for future developments in the field.