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Deep Learning in Machine Vision

Machine Vision

We take it for granted, but our eyes and brain work together in ways that almost seem miraculous when you consider them. The human eye is capable of receiving light and converting it into information that is streamed instantaneously to the brain, which interprets that data just as quickly and prompts the body to react to whatever is seen. After hundreds of years of industry and innovation, only now is technology beginning to catch up to nature in this regard. Recent breakthroughs in artificial intelligence have led to some exciting developments in what is being called “deep learning.” In the manufacturing sector, the marriage of deep learning and existing machine vision tech is helping automation take another leap forward to machines that may soon have more in common with people than we ever thought possible.


Understanding Deep Learning and Neural Networks

Just like people, artificial intelligence wasn’t born knowing or understanding much on its own. It has to be taught the same way humans are, by introducing it to huge amounts of data. Advanced neural networks that simulate the functions of the human brain receive this information and “learns” to identify patterns that it uses to make connections with new information it encounters later. For example, showing an AI a single picture of a duck and telling it what the animal is only means the algorithm can successfully identify that specific drawing as a duck. However, training the program by showing it thousands of different pictures of ducks enables it to identify the common characteristics, such as the webbed feet, the wings, the bill, etc. Based on this training, the algorithm should be capable of identifying a duck even if it had never seen that specific image in the past. 

The computational power of such a neural network is massive compared to computers that run traditional automation systems, and pairing it with a machine vision system has the potential to change the way manufacturers interact with their equipment. By integrating deep learning, machine vision technology is poised to become much more versatile and effective for the manufacturing industry.

Applications of Deep Learning in Machine Vision 

Prior to these recent advancements in AI machine vision, cameras were used in many automation applications, but in limited capacities. Traditional machine vision was capable of identifying certain aspects of components or products for sorting purposes, but these were relatively simple functions. Today, however, the integration of a deep learning algorithm has expanded the utility of machine vision systems by enabling them to go beyond image classification. 

Deep learning machine vision systems can serve a variety of roles within a manufacturing environment. Some of the most common machine vision application types include: 

  • Quality Assurance: The advanced pattern recognition capabilities of this technology make it ideal for QA. A machine vision inspection platform can identify defects on parts or products almost instantly, saving a considerable amount of time compared to traditional visual inspections. 
  • Product Assembly: Thanks to the use of a deep learning model, modern industrial machine vision systems are capable of providing much more accurate and detailed control over robotic assembly arms. Rather than needing all components to be in a precise location for the robot to find them, these machine vision systems immediately identify the parts and assemble them in the most efficient manner. 
  • Machine Health Monitoring: The same principle that enables machine learning to spot defects in products also can be applied to manufacturing equipment. Computer vision platforms can keep a close eye on components and alert technicians to any signs of wear and tear before they lead to failure. This means preventive maintenance can be delivered with greater accuracy and efficiency. 
  • Workplace Safety: Machine vision also can be used to ensure employees are adhering to proper safety protocols. Cameras in production areas can monitor employee movements and determine when unsafe behaviors occur. 

Transfer Learning vs. Reinforcement Learning 

No matter how sophisticated they are, machine learning needs to be trained before it can be of use to manufacturers. The software needs to review a substantial amount of data to be able to make decisions autonomously, and starting from zero with every new application can be a massive undertaking. Fortunately, there are ways to train machine vision systems that allow them to use information they already have without needing to start from scratch. 

  • Transfer Learning: In the simplest terms, this involves training an algorithm to transfer the skills it learned on one task to a similar job. For example, a system that is trained to identify defects in nails can be taught to do the same thing for screws simply by showing it the difference between nails and screws. This means the system can carry over what it knows about defects and apply it to something similar but unique. 
  • Reinforcement Learning: Best suited for more complex applications, this model mimics the trial-and-error process of problem-solving people use. The system is told what the optimal outcome is and learns to perform the task in the most efficient and effective way by being told what it did correctly and what it got wrong. In effect, the algorithm is rewarded or punished based on its performance and learns to adjust its routines. 

Thanks to the integration of a deep learning system, a modern machine vision solution becomes capable of so much more than it was in the past. In essence, it gives automated systems the ability to think about what they see and make better decisions as a result. As manufacturers look for better ways to become more efficient and productive, it’s easy to see how deep learning machine vision technology will play a significant role. 


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