ALL ABOUT BOUNDING BOX ANNOTATION

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When it comes to machine learning, the ability to train data plays a crucial role in order to improve the performance of Artificial Intelligence. Image annotation is one such method where one annotates the images containing the data of interest to make them understandable to the machines. There are various techniques to train data through image annotation. To maintain accuracy, it is crucial to use the right tool for this technique. Let us have a look at one such well-known technique in this blog.

 

WHAT IS BOUNDING BOX ANNOTATION?

 

Bounding boxes is one of the most widely used techniques used in machine learning and deep learning. They are commonly used in object detection and localization tasks. Bounding boxes are rectangular boxes, used to define or outline the location of the targeted object with the help of X and Y coordinators.

 

Bounding box annotations make it easier for computer algorithms to find their target, by highlighting, localizing, and determining their collision paths, etc.

 

The bounding box is easy to create. It can be applied to almost any conceivable object, and it can substantially improve the accuracy of an object detection system.

 

Examples

 

1. We all have come across autonomous cars that can navigate through the world on their own. These autonomous cars actually use bounding boxes to navigate and identify objects.

 

2. Bounding box annotations are also used in video games. For example in shooting games, shooters shoot at “hitboxes” with the help of bounding boxes.

 

HOW ARE THEY DONE?

 

In bounding box annotation, annotation is done through a rectangular drawing of lines from one corner to another on the targeted object in the image based on its shape to make it fully recognizable. In simpler words, a labeller draws a box over the objects of his target on the requirements of the data scientist. There are two types of annotations:

 

2D Bounding Box Annotation

 

In the 2D bounding box annotation method, the annotator draws rectangular boxes around the target objects or objects that he/she wants to be detected. These rectangular boxes are put to define the location of the object within the image. Object classification and localization models can be trained using bounding boxes.

 

3D Bounding Box Annotation

 

3D bounding box annotations are similar to the 2D ones except, they can show the depth of the target object by back-projecting the bounding box on the 2D image plane to the 3D one. The 3D space is extremely beneficial in distinguishing features like volume and position.

 

WHAT ALL TASKS REQUIRE BOUNDING BOX ANNOTATION?

 

● Object Detection
● Image Classification
● Line/Boundary Detection
● Segmentation
● Landmark Recognition

 

DAY-TO-DAY USES OF BOUNDING BOXES

 

Bounding box annotation is used in almost all walks of our lives, such as:

 

-Autonomous Vehicles
-Insurance Claims
-Agriculture
-Ecommerce & Retail
-Healthcare

 

Excited to know the different uses of bounding boxes image annotation? Want to know how they are used for object detection and classification? Let’s explore this in our next blog.

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