All Eyes On Vision AI In 2022

Predictions For A Year Of Pivotal Growth For Computer Vision

Carlos Anchia, Co-Founder and CEO at Plainsight

  Carlos Anchia Sixgill Chief Executive Officer (CEO)

As we conclude 2021, the arrival of Omicron suggests that an evolving version of the pandemic is an ongoing reality. As it has been said many times, in many ways, this is the new normal. Over these last two years, the adoption of computer vision has accelerated, in part, because of its ability to take on pandemic-related challenges such as monitoring social distancing in public places and ensuring COVID-related cleaning and PPE compliance. Enterprises are also deploying vision AI models to automate and improve analysis of video-driven business processes like defect detection in manufacturing, traceability in supply chains, livestock management in agriculture, task optimization in warehouses, automation in restaurant drive-thrus, and shopper personalization in retail.

Humans will continue to be the intelligence behind a lot of this work, but we will enhance human capabilities with the accuracy of machines with machine learning for work that is prone to human error. Businesses will choose to run operations leveraging cameras to optimize employee placement and assignments to mitigate exposure to physical or mental risks. For example, vision AI confirms and helps enforce masking compliance at the entrances of buildings. It can also detect and alert to workplace hazards to protect workers and reduce workplace injury.

Looking ahead, here are three predictions for 2022:

If 2021 was the year of investments in vision AI, we expect 2022 to be the next phase of industry maturity and consolidation across products. Several specialized product companies will come together to form end-to-end platforms that simplify the application and rapid iteration of vision AI solutions. While the use of video cameras to capture real-world information has been around for years, we’ve optimized the value of this unblinking, consistent data source by integrating visual data with AI intelligence for accurate, real-time, actionable insights.

Second, conversations around ethical AI and ethical data analytics have attracted tremendous attention from the public. We will see greater conversation between AI companies, academia, and policymakers to come to an agreement on an ethical framework for the use of AI. There are two purposes to seeing this through. First, having a common regulatory benchmark for AI will allow businesses to feel confident that they are using AI ethically, and second, it builds accountability for AI companies to minimize bias.

 Third, we will continue to be amazed by the innovative use of vision AI. This year, in agriculture alone, cameras and vision AI models were used for everything from identifying individual cows to counting sheep, to tracking animal health, to harvesting robots, to identifying pests and predicting farm production yields.

 We expect to see an increased application of vision AI in the world not currently monitored by pre-installed cameras. For example, use cases include installation in the mountains of California to detect early warnings of fire, to cities leveraging vision AI to better assess traffic and congestion conditions.

Vision AI is one of the most exciting areas of technology due to its ability to help businesses and organizations manage and take advantage of the valuable insights hidden within the explosion of images, video, and other rich media. Companies and governments are betting big on Vision AI because of its potential to see beyond the surface; with one caveat, we should commit to developing this technology responsibly using inclusive teams and ethical guidelines.

More Plainsight Blog Posts:

5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

5 Ways Agribusinesses Can Prevent Recalls, Shutdowns, and Delays with Vision AI

Investments in computer vision technology can help agribusinesses and food manufacturers of all types spot signs of trouble early and stop costly, potentially deadly recalls before they happen. Deployed across the production and manufacturing cycles, these models can detect hazards ranging from contaminants and foreign objects to defective equipment and non-compliant behavior. Organizations capture hundreds of thousands of hours of visual data in the form of video footage and imagery every day, and computer vision allows these businesses to put this data to work for process transformation.

How Can Vision AI Predict and Prevent Supply Chain Disruptions?

How Can Vision AI Predict and Prevent Supply Chain Disruptions?

Introducing computer vision across the supply chain can help manufacturers predict and prevent the types of conditions that lead to these kinds of disruptions. From potential contaminants and foreign objects to defective products and packaging to unsafe or unsanitary behavior, computer vision is the key to recognizing supply chain obstacles early and stopping shortages in their tracks. AI solutions could prove especially useful in volatile, high-production periods where errors and disruptions are both especially likely and especially costly.