Bounding Box

What is a bounding box? The bounding box is an imaginary rectangular box that contains an object or a set of points. When used in digital image processing, the bounding box refers to the border’s coordinates that enclose an image. They are often used to bind or identify a target and serve as a reference point for object detection and create a collision box for that object.   What are some common use cases of bounding box?

• Object Localization for Autonomous Vehicles Driving: The bounding boxes are typically used in training self-driving car vision models to identify different types of artifacts on the road, such as traffic signals, lane barriers, and pedestrians, among other items.
• Ecommerce or Internet Shopping Object Detection: Things sold online are used to annotate with bounding boxes to recall what clothing or other accessories buyers are wearing.
• Detection of Car Loss for Insurance Claims: Types of vehicles like cars, bikes, etc., that have been damaged in an accident can be tracked using bounding box annotated images. Machine learning models that have been trained with bounding boxes can learn the intensity and position of losses to predict the cost of lawsuits so that a client can provide an estimate before making a lawsuit.
• Detecting Indoor Items: Bounding boxes detect indoor items such as beds, desks, benches, cabinets, and electrical devices. It lets computers get a sense of space and the kinds of items that are out there and their location and dimension, making it easier for the machine learning model to identify those items in a real-life situation.
• Robotics and Drone Imagery for Target Detection: Image annotation is often frequently used to mark items from the viewpoint of robots and drones. Autonomous devices such as robots and drones can classify several objects on the planet using photographs annotated with this technique.