Images and videos are becoming the fastest-growing source of data for public and private sector organizations, giving rise to more and more employment of computer vision technology. It is expected that it will prosper and become a US$ 48.6 billion-industry by 2022. Computer Vision is continuously making changes in several major industries including security, retail, and healthcare.
According to McKinsey, AI and computer vision adoption are “rapidly taking hold across the global business.” Recognizing its potential, key players of the tech market are employing the technology for tremendous growth prospects.
So if you are planning to adopt a computer vision for your business process, here are a few things you should consider to driven perfection in the system.
• For a vision project, it is necessary to make sure you understand the illumination needs or else the project might end up incurring additional hardware costs.
• Controlling the software quality is also essential and it is more important than hardware control.
• There should be proper a vision inspection system.
• The company should not adopt such systems that are designed for specific parts. They must be programmed to serve various parts.
• The choice for vision hardware should be made precisely for the environment.
• While considering a vision system, one should consider the effects of Windows upgrades. Notably, a complete backup system will provide an additional five years of life to the vision system.
As quoted in one of the articles of Forbes, there some more considerations to make while bringing computer vision systems to perfection.
It is essential to consider the complexity of any computer vision system and carefully structure the strategy behind it. This will help mitigate the risk of failure from the beginning.
According to Chris Ciabarra, CTO of Athena Security, the three main components of a successful computer vision system are
To determine the quality of computer vision models, the data used for training should be of the utmost quality. According to a report from the BCG Henderson Institute, a number of companies have no understanding of the importance of data and training to make a good computer vision model successful.
Chris Ciabarra cites in the article that, “computer vision required enormous volumes of data, and we soon realized that using pictures and videos available on the internet was not enough to properly train the computer vision. We had to hire a film crew and actors to shoot on different locations and from various angles to produce more than 50 percent of the data we needed internally.”
To run a successful model, computing infrastructure, both software, and hardware, must be in place. The voluminous data and its management require a lot of computing power which tests the potential of both the elements.
A research published by OpenAI (via VentureBeat) in 2018 revealed that there has been a rapid increase in compute power since 2012. This increased power is driving advances in computer vision and AI today.
As AI is a new and still emerging technology, it is hard to meet the demands of computer vision specialists with appropriate skills and training. Also, the complexity that comes with computer vision programs, calls for several groups of specialists to work together.
It is necessary for companies to look outside of their respective regions in order to discover valuable talents globally who are qualified and well-versed in such skill.