Deep Learning with Computer Vision
What is driving growth in computer vision?
Growth in visual data is expected to explode. It is forecast that 44,354,881,622 cameras will exist in the world by 2022. In most scenarios, 99% of the visual data captured will not be valuable but 1% of the visual data at any given time can be extremely valuable. Over the next five years there will be a proliferation of cameras integrated into products across industries and markets. Nearly all inanimate objects will be begin to see, creating vast amounts of visual data across the visual technology ecosystem.
The engineering team at Stealth Technologies have deep capabilities in computer vision.
Computer vision is not one technology, but several that are combined to create intelligence. In the end, computer vision is a method for acquiring, processing and analyzing images, and can automate, through machine learning techniques, what human visual analysis can perform.
One way to imagine computer vision technology, industry analysts say, is as a stool with three legs:
- sensing hardware;
- software (algorithms, specifically); and
- the data sets they produce when combined.
Stealth Technologies uses computer vision for autonomous automation
Capture the image
Visual technologies are any technologies that capture, analyse, filter, display or distribute visual data for businesses or consumers. They typically leverage computer vision, machine learning and artificial intelligence. The majority of the data brains analyse is visual, and therefore the majority of the data needed for artificial intelligence to have human (or better than human) skills, will rely on the ability for computers to translate high quality visual data.
Understand with AI and ML
Computer Vision is a multidisciplinary field that could broadly be called a subfield of artificial intelligence and machine learning, which may involve the use of specialised methods and make use of general learning algorithms to understand images and video.
The Stealth Team are engineering specialists who have specialities in robotics, vision and automation, including the robotic control of vehicles. The team have worked within research and development projects which have successfully implemented a vision-based driver assistance system for lane keeping and collision avoidance within a BMW X5.
Rapid adoption of Computer Vision
|Mining||Around the world, computer vision has become critical to mining, assisting in automating manual processes. Examples are using the processes of computer vision and deep machine learning. On-board cameras are placed on loaders to track variables such as loading time, hauling time, dumping time and travelling empty time.|
|Manufacturing||In manufacturing, businesses use computer vision to identify product defects in real time. As the products are coming off the production line, a computer processes images or videos, and flags dozens of different types of defects — even on the smallest of products.|
|Health Care||In the medical field, computer vision systems thoroughly examine imagery from MRIs, CAT scans and X-rays to detect abnormalities as accurately as human doctors. Medical professionals also use neural networks on three-dimensional images like ultrasounds to detect visual differences in heartbeats and more.|
|Insurance||In the insurance industry, companies use computer vision to conduct more consistent and accurate vehicle damage assessments. The advancement is reducing fraud and streamlining the claims process.|
|Defence & Security||In high-security environments like banking and casinos, businesses use computer vision for more accurate identification of customers when large amounts of money are being exchanged. It’s impossible for security guards to analyse hundreds of video feeds at once, but a computer vision algorithm can.|