Industrial computer vision is the process of using AI to extract valuable information from images. The global average for industrial robot installations is predicted to grow by 8% per year. That’s more than three times the rate of installation growth seen in the 1990s. With more and more companies investing in robotics and AI, we can expect this trend to continue. Industrial computer vision systems are becoming increasingly important as they can increase productivity, reduce costs, and improve safety.
What is Industrial Computer Vision?
A computer vision system does three important things: It recognizes and classifies objects in images. It identifies a series of objects based on their similarities. And it identifies edges and corners of objects. EAMVision in previous posts talked about the first two of these in the previous article on Machine Vision for Manufacturing. But what about the third? As with many other computer vision systems, this one is the hardest to explain. The Image Recognition Problem Image recognition (more commonly known as image classification) is one of the most computationally difficult problems to solve. Image recognition systems typically have more than 100 billion mathematical operations. Just to make things worse, a human in the system has to examine the millions of image frames before classifying an object.
The Importance of Industrial Computer Vision
In an increasingly competitive business environment, companies can no longer take the easy way out. It’s time to bring the machines closer to the humans. After all, if you aren’t in the loop when something goes wrong, you might not be able to fix it. Some people might argue that using AI will lead to job losses, but the machines aren’t taking anyone’s job—it’s our way of working that has changed. Through AI we can develop and manufacture products much more quickly. Think about what the manufacturing floor is like. With traditional robotic applications, robots have limited interaction with the human. With industrial computer vision, this no longer needs to be the case. That means more accidents can be avoided, so fewer human workers need to be put at risk.
How Industrial Computer Vision Works
While AI has been applied to medical imaging, it’s only in recent years that industrial systems have started incorporating computer vision systems to help with tasks such as inspecting materials, packaging, or customer-facing tasks like robotic vision and chatbots. While traditional vision systems used for such tasks either require a large amount of computing power or work best with static, zoomed-in images, computer vision systems work more efficiently and require less data processing. Industrial computer vision systems are an evolution of these types of systems and can tackle even more complex, real-world situations.
Applications for Industrial Computer Vision
Common applications for industrial computer vision are everything from inspecting equipment, through to augmented reality glasses. These vary by industry. Here are just a few of the different applications that you can expect to see in the near future. Prove the quality of parts Manufacturers often inspect the manufacturing process before it’s sent to the customer. Inspections should be good enough to verify that there aren’t any defects in the product, and that it meets the requirements. Industrial computer vision systems are well-suited to the process, since they can detect problems such as overspray, pinholes, and inconsistencies. Accurately detect defects It’s difficult to accurately check components like buttons, batteries, connectors, and cables.
Conclusion
Although Machine Vision has been on the scene for quite a while, the world’s leading AI vendors have taken very different approaches to improve their Machine Vision. IBM, Intel, Google, NVIDIA, and Microsoft all have different strengths, with IBM offering the best long-term prospects. The mix of these vendors will determine which Machine Vision platform will come out on top in the coming years. EAMVision’s bet is on Google AI Vision. Google is building its cloud services business with Google Cloud Platform. By designing its cloud platform so it can scale to handle the growing demand for deep learning, Google is giving itself a distinct advantage over other cloud computing vendors. Google’s position is further strengthened by the fact that it’s already the number one player in hardware and software AI.
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