Annotation with Box

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. Bounding box is one of the most popular and recognizable image annotation method 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 all of 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 they can substantially improve the accuracy of an object detection system.

Bounding boxes are used in computer vision annotation for the purpose of helping networks localize objects. Models that localize and classify objects benefit from bounding boxes. Common uses for bounding boxes include any situation 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 model to recognize the various types of objects comes on the roads like traffic signals, lane obstacles and pedestrians 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 box and recognize the clothing or other accessories brought by the customers. All kind 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 accident 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 that a customer can get an approximate idea before claiming the insurance.

Indoor Objects Detection

The use of bounding boxes is also very much high in detecting the indoor objects like furniture, tables, chairs, cupboards and electronic systems. For machines it helps to get an idea of a room and what kind of objects are placed there with their position and dimension making easier for ML model to detect such things easily in real-life scenario. Images annotated with bounding boxes deep learning technique helps 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 drones point view. Images annotated with this technique are used to train machines like robots and drone which can identify the variety of objects on the earth. The varied range of objects can be captured into the bound box making easier for robots and drones to detect the similar physical objects from the distance and behave accordingly.

Scroll to top