CEO Everguard.ai, whose entrepreneurial skills have helped him develop teams, products, and new markets across technologies and industries.
Computer vision has exploded onto the technology scene over the past decade. Considered one of the most powerful types of artificial intelligence (AI), it has become the technology solution of choice for some of the most complex issues facing industries today.
From health care to automotive to manufacturing, computer vision has made great strides to solve real-world problems. One important way that computer vision is advancing is helping heavy industrial facilities protect their most important assets: their people.
The Evolution of Computer Vision
Computer scientists first began deep explorations of computer vision in the 1960s. As the name implies, the goal was to help computers “see” and gain understanding that rivaled human observations. The challenge was finding a way to scale this technology commercially. Early computer vision technology relied heavily on manual training. From building rules-based classification techniques to manually selecting relevant features of an object, the process was time-consuming and difficult to improve or alter. The simplest of changes, such as object size or orientation, caused failure.
Deep learning has transformed computer vision in the past decade and provided a way to commercialize the technology for industrial applications. The traditional ML flow involved feeding the system image data (input), requiring manual extraction of features and labeling of these features. An engineer then coded each component as a rule that could detect the elements within the image.
Deep learning simplifies this process. The deep neural network training process uses massive amounts of data sets and numerous training cycles to teach the machine what an object looks like. Instead of manually extracting features, the algorithm automatically extracts relevant parts. One of the most valuable capabilities is that the deep learning model can be applied to previously unseen images and still produce an accurate classification. In contrast, a traditional machine learning flow would fail.
We can attribute deep learning advancements in computer vision to the massive amount of image data we have today. Feeding hundreds and thousands of well-labeled images to a deep learning system enables it to understand the exact features that make up the larger image. The more image data fed, the more it learns, ultimately resulting in higher performance. These advancements have driven the commercialization of computer vision technology across many industries.
Computer Vision In Safety
One of the most exciting and valuable applications of computer vision is its use in the safety industry. For many years, safety relied heavily on a reactive approach of individually reacting to each accident or injury with insights gained from the last incident. While this method eventually leads to safety improvements, an accident or injury must occur for the safety program to understand how to avoid it. Just as computer vision technology continues to evolve, it provides a way for our safety programs to do the same by implementing a proactive approach, lessening the risk of injuries and accidents by preventing them before they happen.
One of the most valuable aspects of using computer vision in safety is the analytics it provides. Many safety programs have had to rely on a reactive approach in the past, reacting to the severity of an event with the last serious incident’s investigative findings. They typically must use lagging indicators, such as an injury once it has occurred. While the data collected is valuable, lagging indicators aren’t effective in preventing accidents; instead, they prove useful to measure effectiveness.
Leading indicators are the actionable data points that identify a safety issue before the incident occurs. AI-enabled computer vision and leading indicators go hand-in-hand. The data collected by the AI-enabled computer vision provides accurate, real-time data that help create leading indicators. Leading indicators allow preventative action to address potential hazards before they cause an incident.
Overcoming Computer Vision Challenges
As with all new technology, implementing computer vision in a space as complex as safety has challenges. Lack of data is a major one. For a model to be as accurate as possible, it requires a massive amount of data. But we don’t always have a massive dataset. This is where data augmentation comes in. Using data augmentation, we can expand our dataset with the already-collected images.
For example, imagine we are training a model to identify cats. We have a limited number of cat images, but we can augment those images to provide our model more data. This can be as simple as flipping or rotating an image horizontally or vertically, scaling inward or outward or even cropping the image.
Another challenge is providing data for dangerous use cases. Imagine training a model to identify when a person is in danger of being run over by a piece of mobile equipment. We can’t provide this kind of data without putting a person in danger. Instead, we can create a 3D model to simulate data to augment a real data set to improve the model. While most experts agree that having real-world, natural data is best, data augmentation is a safe solution.
With these challenges in mind, here are three considerations before investing in computer vision AI:
1. Find the problem.
Take a hard look at your company. What are potential problems? What are you looking to achieve? Computer vision AI is a significant investment. These answers help find a solution aligned with your company’s goals.
2. Ask about data.
Once you understand the problem, ask: Do you have data? The most powerful and accurate systems are those trained with large datasets. Before making such a substantial financial investment, ensure you have access to applicable data.
3. Understand the investment.
In addition to being a large financial investment, computer vision AI also requires a time investment. The AI model must be trained with supervised learning. Training can take a few days to a few weeks, but the process is on-going. If a new use case is discovered, the model must be trained on that use case. These systems also require regular maintenance. Make sure that your team is on-board with the time commitment.
The Future of Computer Vision
We have made revolutionary advancements with computer vision technology over the past decade. However, we’re still in the very early stages of understanding just how powerful computer vision will be for the safety industry. As we continue to make discoveries on computer vision’s capabilities, there is no better place to be than at the forefront of this movement.