Explain the evolution of the YOLO (You Only Look Once) object detection framework, detailing its core concept of single-pass processing for speed and efficiency.

They cover key architectural components like the backbone, neck, and head, the use of anchor boxes (in many versions), and the structure of its output tensor.

The text also compares YOLO's speed and accuracy to other methods like SSD and Faster R-CNN, outlines common challenges in implementation (such as small object detection and dataset imbalance), and discusses practical applications across various fields and future trends in AI vision.