An increasing part of the participants in the production and warehouse direction comes to an understanding of the need for the full or partial robotization of work processes. Of course, robotics and automation are becoming challenging to replace when it comes to non-stop work with large loads.
AI and computer vision annotation tools ensure the movement of objects within warehouses and production facilities without human intervention. At the same time, the requirements for the accuracy of data annotation processing to avoid collisions of things or the occurrence of confusion remain relevant expectations.
Following the market's demands and remaining the flagship among the companies providing annotation services, Keylabs has expanded the list of image and video annotation tools, supplementing it with one more - cuboid.
In this blog, we will first deal with the very concept of what a cuboid annotation is and how it helps to perform operational warehouse tasks. And finally, let's look at the industries where the use of cuboid annotation is the most promising.
The cuboid image annotation tool aims to interpret 2D images as 3D objects based on cameras’ data. Each 2D object can be decomposed into cuboids - rectangular parallelepipeds, helping to feel the position of things in space as precisely as possible.
Cuboid annotation finds its use in all areas. Still, it remains mostly in demand in those industries where we are talking about perspective, helping better annotate cars, buildings, indoor objects, etc. Thus, cuboid annotation is training dataset as realistically as possible for the real world.
Warehouse work with boxes of various sizes is the perfect case for cuboid annotation. Container boxes, positioning to objects, moving boxes within the production area, — AI cuboids help to distinguish between objects and avoid obstacles accurately.
Cuboid annotations provide data that depicts the movements and interactions of objects. Let's take a closer look at what cuboid capabilities are needed for secure synchronization:
- Obstacle Recognition: Obstacle recognition guarantees the identification of objects and an algorithm for the appropriate reaction to a particular thing.
- Environment Perception: Annotators create training data using a cuboid annotation method. Each frame helps AI correctly perceive the situation and build a behavior scenario.
These possibilities open up the potential of using cuboid annotation in other areas.
Self Driving Cars
Cuboid Annotation helps to navigate autonomous vehicles in a real-time environment. Accurately determining the distance between vehicles and other obstacles is an invaluable tool for managing the movement of a self-driving car on the road.
Based on 2D camera data, cuboid annotation allows you to translate images into 3D, showing realistic representations of objects and their position in space relative to each other, whether it is a sofa, a table, or ornate antique furniture.
Keylabs Service Annotation
Of course, none of the options will do their job with poor quality annotation services. The use of the cuboid labeling tool by developers implies access to high-level accuracy services from annotation providers with their team and experienced management, such as Keylabs.
Keylabs provides highly accurate services, clear and transparent annotation processes, the ability to adjust annotation accuracy and speed, and self-monitor the process if necessary.
For a comprehensive personal demo, please contact your Keylabs account manager.