Bounding Box or Cuboid: Which Image Annotation Tool is the Best For the Automotive Industry?

Cuboids are the best choice for the automotive industry. That’s because cuboids literally provide more in-depth, detailed metadata. Cuboids drawn around objects like cars are efficient. Those cuboid shapes can also create an extra margin of error for things like collision detection and other calculations.  

That is not to say that bounding boxes and rotating bounding boxes aren’t also useful. They definitely are, and so are other shapes and techniques. In general, more data and more metadata is best, of course. You should use the best image annotation tool for the task.

Cuboids are usually the best choice for the automotive industry because most cameras capture flat information in two dimensions. Cuboids provide three dimensions of information. So even a flat road sign still has some depth.

It is not a choice of which is “best.” It is also a matter of which is the right tool to provide the right data for the tasks. As an analogy, you would not want to pound a screw in with a hammer if you were a carpenter. Instead, you would like to drive it in with a powered screwdriver.

AI in the automotive industry usually needs that depth perception added and processed. The AI usually needs to know the volume of space something takes up. The cuboid should show that objects take up a bit more space to be safe. That is usually even better than the AI knowing an object's exact shape and size.

Certainly, it is better than ignoring depth or having a cuboid that does not fit completely around an object. That can cause costly problems with your data training and set that can take a long time to test, find and debug.

Use Cases and Product Features Supported by Cuboids:

• Autonomous Driving
• Critical calculations such as the distance from and between various objects and barriers.
• Provide the volume of space that objects take up.
• Collision Avoidance
• Precise object identification.
• Track the movement and relative speed of other objects.
• Provide depth perception
• Enhance the overall perception of computer vision.
• Provide more intricate, detailed information about objects.

Choose the Best Image Annotation Tools for Your Project

Creating an AI for the automotive industry and related fields is complicated enough. You could leave some of these questions to the data annotation companies. However, for a data annotation company to best serve you, it is a good idea to know exactly what you need them to do.

So, you should have some familiarity with the best image annotation tools. In addition, you should know the best data annotation techniques for your data training and model. Cuboids are essential for any computer vision and AI applications for the automotive industry. Cuboids and bounding boxes are also similar to each other.

The big difference is the intricacy and detail of information added by the third dimension of cuboids. Therefore, cuboids are critical to creating and training any automotive AI. Because it can't be done without Cuboids, that makes them the "best" compared to flat bounding boxes.

A cuboid can be thought of as a shape that exists in augmented reality (AR) that is made up of 6 bounding boxes in three (virtual) dimensions. The amount of additional value and metadata that cuboids provide is at least six times that of the most complex rotating bounding box.  

Again, this isn't to put down bounding boxes at all.  The purpose of this article is really about education.  Bounding boxes are also very helpful and important image annotation tools for computer vision. There is a good reason that bounding boxes are so popular and used in many computer vision features of AI in the automotive industry.

Object detection often uses bounding boxes in combination with other data augmentation techniques.  Data annotation is a skilled and detail-oriented task. Misaligning cuboids or drawing them too closely so that the object does not fit inside the box can cause serious issues.

Those kinds of problems can cost you a lot of time and money. That's why it is a good idea to consider outsourcing your data annotation project to experts. You will want the best team with the best,  most advanced image annotation tools for your project needs.