Annotation with BOX

Annotation With Box

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Annotation With Box

As it sounds like, the labeler is asked to draw a box over the objects of interest based on the requirements of the data scientist.

The bounding box is one of the most popular and recognizable image annotation methods used in machine learning and deep learning. Using bounding boxes annotators outline the object in a box as per the machine learning project requirements. It is one of the cheapest and low time taking annotation methods in the industry.

It is one of the most commonly used types of image annotation in computer vision. Bounding boxes enclose objects and assist the computer vision network in locating objects of interest. They are easy to create, declared by simply specifying X and Y coordinates for the upper left and bottom right corners of the box. It is the most versatile and simplistic annotation type. The bounding box can be applied to almost any conceivable object, and it can substantially improve the accuracy of an object detection system.

Bounding boxes are used in computer vision annotation for the purpose of helping deep learning networks localize objects. Models that localize and classify objects benefit from bounding boxes. Common uses for bounding boxes include any application where objects are being checked for collisions against each other.

Some Of the use Cases are:

Object Localization for Autonomous Vehicle Driving

The bounding boxes are widely used in training the self-driving car perception models to recognize the various types of objects that come on the roads like traffic signals, lane obstacles, and pedestrians, and etc. All the visible objects can be easily annotated with bounding boxes to make it recognizable for machines to understand the surroundings and move the vehicle safely while avoiding any crashes even when moving into the busy streets.

Object Detection for E-commerce or Online Retail

The products sold online are also used to annotate with bounding boxes and recognize the clothing or other accessories brought by the customers. All kinds of fashion accessories can be easily annotated with this technique helping visual search machine learning models to recognize such things and provide the other details to end-users.

Vehicle Damage Detection for Insurance Claims

Cars and other types of vehicles damaged due to accidents can be detected with the help of bounding box annotated images. Trained with bounding boxes, the machine learning models can learn the intensity and point of damages to estimate the cost of claims before a customer claims the insurance.

Indoor Objects Detection

The use of bounding boxes is high in detecting indoor objects like furniture, tables, chairs, cupboards, and electronic systems. The use of bounding boxes helps to get an idea of a room and what kind of objects are placed with their position and dimension making it easier for the ML model to detect such things easily in a real-life scenario. Images annotated with bounding boxes help to understand objects better.

Object Detection with Robotics and Drone Imagery

Image annotation with bounding boxes is also widely used to label the objects from robots and drone point of view. Images annotated with bounding boxes are used to train machines like robots and drones which can identify the variety of objects on the earth. A varied range of objects can be captured under the bounding boxes making it easier for robots and drones to detect similar physical objects from a distance and behave accordingly.