A rectangular area within an image, initially defined according to one coordinate system, is often represented in a different, and potentially more useful, format. For instance, an initial detection might yield a box defined by pixel coordinates relative to the original image size. Subsequently, these coordinates could be transformed to a normalized format, ranging from 0 to 1, irrespective of the original image dimensions. This allows for easier scaling and use across different image resolutions. Consider a scenario where an object is detected in a 1000×1000 pixel image, and its original bounding box is [200, 300, 400, 500]. After processing, it may be represented as [0.2, 0.3, 0.4, 0.5] using a normalized system.
This transformed representation offers several advantages. It promotes model generalization, as it becomes independent of the input image size. This allows models trained on one resolution to be applied seamlessly to images of different sizes. This is particularly valuable in scenarios involving variable image resolutions or where data augmentation techniques are employed. Furthermore, it facilitates efficient storage and transmission of bounding box data. Storing normalized coordinates typically requires less memory compared to retaining absolute pixel values. The transformation also streamlines operations such as intersection-over-union (IoU) calculations, a common metric for evaluating object detection performance, as the calculations are simplified when working with normalized values. Its evolution mirrors the broader trend of abstraction in computer vision, aiming to decouple models from specific input characteristics.
