Home >Deep learning + machine vision = next-generation inspection
Deep learning + machine vision = next-generation inspection
21 April 2020
Combining machine vision and deep learning can bring benefits in both operational and ROI terms. Understanding the differences between traditional machine vision and deep learning, and how these technologies can complement each other - rather than compete or replace - is essential to maximising investment
AI, and specifically deep learning-based image analysis or example-based machine vision, when combined with traditional rule-based machine vision, can help identify correct parts, detect if a part is present or missing or assembled improperly, and more quickly determine if those are problems. And this can be done with high precision.
GPUs (graphics processing units) gather thousands of relatively simple processing-cores on a single chip. Their architecture looks like neural networks. They allow to deploy biology-inspired and multi-layered 'deep' neural networks which mimic the human brain.
By using such architecture, deep learning allows for solving specific tasks without being explicitly programmed to do so. In other words, classical computer applications are programmed by humans for being 'task-specific', but deep learning uses data (images, speeches, texts, numbers…) and trains it via neural networks. Starting from a primary logic developed during initial training, deep neural networks will continuously refine their performance as they receive new data.
Accurate outputsDeep learning technology makes very accurate outputs based on the trained data. It is used to predict patterns, detect variance and anomalies, and make critical business decisions. This same technology is now migrating into advanced manufacturing practices for quality inspection and other judgment-based use cases.
When implemented for the right types of factory applications, in conjunction with machine vision, deep learning will scale-up profits in manufacturing (especially when compared with investments in other emerging technologies that might take years to payoff).
So how does deep learning complement machine vision? A machine vision system relies on a digital sensor placed inside an industrial camera with specific optics. It acquires images. Those images are fed to a PC. Specialised software processes, analyses, measures various characteristics for decision making. Machine vision systems perform reliably with consistent and well-manufactured parts. They operate via step-by-step filtering and rule-based algorithms.
On a production line, a rule-based machine vision system can inspect hundreds, or even thousands, of parts per minute with high accuracy. It's more cost-effective than human inspection. The output of that visual data is based on a programmatic, rule-based approach to solving inspection problems. On a factory floor, traditional rule-based machine vision is ideal for: guidance, identification, gauging and inspection.
Rule-based machine vision is great with a known set of variables: Is a part present or absent? Exactly how far apart is this object from that one? Where does this robot need to pick up this part? These jobs are easy to deploy on the assembly line in a controlled environment. But what happens when things aren't so clear cut?
This is where deep learning enters the game:
- Solve vision applications too difficult to program with rule-based algorithms
- Handle confusing backgrounds and variations in part appearance
- Maintain applications and re-train with new image data on the factory floor
- Adapt to new examples without re-programming core networks.
Inspecting visually similar parts with complex surface textures and variations in appearance are serious challenges for traditional rule-based machine vision systems. 'Functional' defaults are almost always rejected, but 'cosmetic' anomalies may not be, depending upon the manufacturer's needs and preference; these defects are difficult for a traditional machine vision system to distinguish between.
Due to multiple variables that can be hard to isolate (lighting, changes in color, curvature, or field of view), some defect detections, are notoriously difficult to program and solve with a traditional machine vision system. Here again, deep learning brings other appropriate tools.
In short, traditional machine vision systems perform reliably with consistent and well-manufactured parts, and the applications become challenging to program as exceptions and defect libraries grow. For the complex situations that need human-like vision with the speed and reliability of a computer, deep learning will prove to be a truly game-changing option.
- Deep learning technology makes very accurate outputs based on the trained data; it is used to predict patterns and detect anomalies
- When implemented for the right types of factory applications, in conjunction with machine vision, deep learning will scale-up profits
- Rule-based machine vision is great with a known set of variables; deep learning comes into its own when things aren't so clear cut