YOLO divides the input image into equal-proportion grids, splitting the image into S×S regular grid cells. Each grid cell is independently responsible for detecting objects whose centers fall within its own area. This design decomposes the global detection problem into multiple local sub-problems, simplifying model learning difficulty and improving inference efficiency.
