Cuboid Image Annotation for Machine Learning

Cuboids annotation provides in-depth information to two-dimensional, flat images and videos for AI and machine learning. Humans are naturally very good at pattern recognition, and human eyes have depth perception. We also have a way of visualizing or understanding a part of an object, like a car or truck, that we’ve seen before. Cuboids are helpful for automotive image annotation.

Manual labeling and annotation are required because computers, AI, and machines have difficulty doing some things humans do. So it is necessary to train AI to do those things with supervised deep learning. Computer vision image annotation for machine learning and AI comes in different forms. Cuboids annotation is preferable because cameras do not have stereoscopic vision.

Supervised machine learning is one way to call how AI is most commonly programmed or trained after they are programmed. It is like if you were given a lot of challenging math homework with all the answers and then asked to figure out how you would get the same answers.

That all requires a substantial amount of data, videos, and images. All of that stuff requires manual image annotation for machine learning. All that data collected, hours and hours of video, it all still requires the kinds of human intelligence we take for granted.

By their very nature, computers, machines, AI, and raw data can not be biased. However, they can reveal or even exaggerate human bias. This isn’t restricted to what you may think of when you read about bias. There is an adage in computer science: garbage in, garbage out.

What may seem like perfectly valid data is not always the same as useful data. If you put the wrong numbers into a calculator, it can’t possibly give the right numbers back. Different kinds of image and video annotation and data labeling should provide the right answers that your AI needs.

AI in the Automotive Industry

Safe, self-driving cars that are commonly accepted everywhere seem like they are always one or two years away from becoming a reality. That is mainly because of the differences between how humans and machines or AI think. There are still many challenges in developing an AI that can recognize vehicles, humans, animals, potential obstacles, street signs, landmarks, speed limits - everything you need to predict its behavior.

An awful lot goes into creating and training an AI for an autonomous vehicle that can understand and navigate the real world and real traffic. Recreating human abilities and human decision making in a machine is difficult and comes with moral problems.

For example, the humans inside an autonomous vehicle may be in a terrible situation, injured to maneuver to avoid running over a mother with her infant. What should the AI choose to do? Should it protect its owner and passengers, or should it kill a baby?

That is an extreme but very possible hypothetical scenario. In such a situation, we trust human beings to make life or death decisions. Human drivers make such split-second decisions every day. An advanced AI driving a vehicle could protect the people inside and the hapless pedestrians. If given a chance, AI would be widely accepted on the road.

Any company that creates a smart AI to drive an electric smart car, autonomous vehicle, or drone would benefit from an image annotation outsourcing service. It will handle the data collection required. As a result, they could focus on the larger challenges and innovation.

Cuboid Annotation Uses for AI

Cuboids are useful for developing an autonomous automobile AI that can:

• determine the dimensions of other vehicles
• tell how fast they are going
• tell how fast other vehicles are going (This may have law enforcement applications.)
• calculate the distance between themselves and those other vehicles
• understand the dimensions of various obstacles that may present themselves on the road
• recognize various obstacles
• calculate the space between obstacles
• avoid obstacles
• recognize street signs and traffic lights
• know how far away a stop sign or traffic light is
• make good decisions
• be widely accepted as trustworthy by humans

This also has other applications, for example, traffic analysis using computer vision and traffic cameras or analyzing images and video records for insurance or legal claims.

Conclusion

Cuboid image annotation is critical in creating and training an AI that is useful in the Automotive industry, whether it is to provide the smarts for a self-driving smart car, traffic analysis, analyzing images and video for insurance claims, or something else. Innovative companies in the automotive industry face real challenges in creating useful AI.

Enterprises would benefit from an image annotation outsourcing service. For example, they will handle huge amounts of collected data, images, and videos required. Of course, they will need their programmers and engineers to innovate and meet the challenges of creating an AI capable of making human-like life or death judgments and decisions.