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Why do machines need vision? Vision is the main sense. Machines must be able to understand humans and provide the support they need, so they must be able to observe and express in the visual category. The specific form can be a small camera that helps the blind to "see" and understand the world around them, or a home monitoring system that can accurately distinguish stray cats, moving branches and thieves.
As electronic devices become more and more important in people's daily lives, we also find that more and more device applications fail because they do not have enough powerful visual functions. For example, drones collide in the air, robot vacuum cleaners do not suck up. The sucking thing. 
Machine vision is a branch of artificial intelligence that is rapidly evolving, designed to give machines a vision that rivals humans. Machine vision has made tremendous strides in the past few years as researchers have applied specialized neural networks to help machines recognize and understand real-world images. Today's computers can do a wide variety of things in visual recognition, from recognizing cats on the web to identifying specific faces in many photos. However, this type of technology still has a long way to go.
Currently, machine vision is moving out of the data center for a variety of purposes, from the automatic driving of drones to food preparation.
Basic image classification is much simpler, but when extracting meanings or information from complex scenes, machines face a series of new problems. The problem of illusion is a good example of the long road to machine vision.
For example, when a person sees a contour image of two face-to-face faces, they see more than just an abstract shape. Their brains will be further interpreted to allow them to identify multiple parts of the image, see two faces, or see a vase.
But for machines, such images are very difficult to understand. The basic classifier doesn't distinguish between two faces and a vase. It sees objects such as hatchets, hooks, bullet-proof vests, and even guitars. The system does not determine which objects are in the image, which means that the identification of such images is extremely challenging for the machine.
In addition, just like complex images, the real world is also very messy. Normal navigation in it can be achieved without the light development algorithm analysis data, it needs to have a clear understanding of the real scene, and then can act accordingly.
Robots and drones face a large number of such obstacles, and overcoming these challenges is a top priority for those involved in the artificial intelligence revolution.
With the continued popularity of technologies such as neural networks and specialized machine vision hardware, the gap between machine vision and human vision is rapidly shrinking. Soon, there may even be robots with more visual power than humans, capable of performing a variety of intricate tasks and being able to operate fully automatically.
Machine vision will be the next frontier of artificial intelligence
The technology website Venturebeat published an article saying that artificial intelligence has developed strongly in the past year, bringing more and more benefits to people. In the future, machine vision will be the next frontier of artificial intelligence. With the development of this type of technology, new artificial intelligence-driven devices will emerge next year.