As Demand for Computer Vision Grows, What Key Technologies Are Trending?

Computer vision is growing in popularity fast. It’s likely part of your everyday life. Do you have a phone that you unlock with facial recognition? Does your car let you know when you’re drifting out of your lane? Using a virtual background on your Zoom call?  These are all examples of computer vision in everyday life.

Aside from the commercial features made available to consumers, computer vision (CV) is having a direct impact on global industry. The increase in visual data, enhanced neural networks, and low-cost chips will continue to fuel the growth of computer vision. Here are some of the newest trends in technology related to CV.

Computer Vision Technology Trends

There are countless technologies related to computer vision. Engineers have come up with all kinds of solutions for challenges posted to CV systems.

Computer Vision as a Service. CVaaS is a type of software-as-a-service that lives in the cloud. Instead of having to build their own computer vision platform, businesses can rent one. It’s pay-as-you-go and makes computer vision more accessible for all. 

Convolutional Neural Networks. CNNs are deep neural networks that are often used to analyze images. The neurons of a CNN can improve through learning. CNNs have given CV a boost in recent years. Their ability to process images is made possible by a mathematical process called a convolution. CNN’s can provide benefits in a wide variety of industries. In healthcare, CNNs can rapidly process hundred of X-rays, MRIs and other medical images and can be as effective as a human doctor in providing a diagnosis. In fact, health issues such as brain tumors, diabetes, Parkinson’s diseases, breast cancer, and many others are being diagnosed successfully with the help of Computer Vision and CNNs. Agriculture is another area where Computer Vision and CNN’s are providing great benefits such as determining the health of a seed to the health of the crops, as well as identifying areas with fertile soil or the presence of water.

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Graphical Processing Units. GPUs can process large blocks of data at high speed. This is especially important for training neural networks. GPUs were originally designed for computer games but their application in CV has accelerated AI training and inference.

Smart Cities. Surveillance technology is being used to improve efficiency in smart cities. Computer vision can quickly alert officials when there are accidents on highways, delays on public transportation, and other kinds of congestion or disturbances.

Image Search. Computer vision algorithms are helping businesses and consumers to find images faster. Neural networks are able to learn what an image comprises and then indexes that information to be found later. The same technology has even come to handheld devices.

Home Security. From consumer doorbells with cameras to more robust security systems, computer vision can monitor what’s going on at home at any time. After detecting movement, the camera can send video to the cloud to alert the homeowner at a low cost.