A Bounding box is one of the simplest and fastest ways to localize an object in a data labeling project.
A bounding box is essentially a rectangular outline drawn around an object or a region of interest within an image. This technique is common to annotate images for machine learning projects. It is mainly employed in the field of computer vision for tasks like object detection and image classification.
To create a bounding box, the annotator or labeler draws a rectangle around the object or region of interest in the image. Bounding boxes are mainly done using data labeling tools.
We typically define Bounding boxes by two sets of x and y coordinates.
Bounding boxes are used to annotate a variety of objects or regions in images. Here are some examples:
- people
- animals
- buildings
- vehicles,
- and many others complex ones!
Today, there are many ways to refer to Bounding boxes:
- Bbox, bboxes
- B-box, b-boxes,
- BB, BBs
- etc.
To provide additional information about the objects or regions represented, bounding boxes are often combined with other types of annotations (or labels). Classes (to identify an object, such as “apple”, “pear”, “orange”) and attributes (to add object-specific details, such as maturity level, occlusion, etc.) are two examples of annotations that we can add to bounding boxes.
Finally, to better localize some objects, it is sometimes interesting to rotate bounding boxes. It is a feature in some labeling tools named “oriented bounding box”.
Synonyms: Bbox; BB
