A bounding box is an imaginary rectangle that serves as a point of reference for object detection and creates a collision box for that object. Data annotators draw these rectangles over images, outlining the object of interest within each image by defining its X and Y coordinates.
Data annotators draw these rectangles over images, outlining the object of interest within each image by defining its X and Y coordinates. This makes it easier for machine learning algorithms to find what they’re looking for, determine collision paths, and conserves valuable computing resources.
Bounding boxes are one of the most popular image annotation techniques in deep learning. Compared to other image processing methods, this method can reduce costs and increase annotation efficiency.