There are two types of machine vision or machine learning systems: rule-based and AI-based. Rules-based systems follow a predefined set of rules to identify and classify objects of images, while AI-powered systems can make intelligent decisions after “learning” from a data set of reference images. But how do these differences impact flexibility, adaptability and output? And which is most applicable for your organization? In this article, we explore the advantages of both learning algorithms, and what considerations you should consider when selecting which system is needed for your application.
What Is Rule-Based Machine Vision?
Rule-based machine learning or machine vision is a cause-and-effect-based model that relies on a predetermined set of rules or algorithms in order to identify and classify images of objects; for example, for manufacturing technology solutions. Rules-based machine vision follows a flowchart-style process to classify images of objects; if X, then Y. This type of machine learning is ideal for tasks involving minimal variation, where it can help to reduce the risk of human error.
Rule-Based Machine Learning Use Cases
Rule-based machine vision is frequently used in manufacturing and healthcare environments. Its ability to process images at speed makes it well-suited to fast-moving production lines classifying high volumes of the same type of object. The rules-based approach is ideal for applications using pre-defined visual data, where there’s no requirement for variation. For example, in a laboratory setting, machine vision can be used to analyze liquid levels in a test tube. A rule could be, “if the test tube is filled to at least a specific level, then let it pass.”
Common use cases for rule-based machine learning include:
- Object classification
- Quality control
- Defect identification
- High-speed image processing
- Measurements
Benefits Of Rule-Based Machine Vision
- Speed: Once rules have been programmed, the system can quickly make decisions based on the presented data.
- Requires Minimal Data Input: It’s straightforward for developers to program a set of rules, with no complex algorithms or large datasets required.
- Predictable: Rule-based machine learning draws on the same rules for every decision, so the system will do exactly what it’s been trained to do reliably.
- Precise and Accurate: Rules-based machine vision reliably follows the given rules and instructions, making it highly accurate in suitable environments.
What Is AI-Based Machine Vision?
AI-powered machine vision utilizes artificial intelligence to make decisions about images; for example, for objects on a production line or automated sample preparation in a laboratory. This type of machine vision is also referred to as machine learning or deep learning.
An AI-based machine vision system is trained to make informed, intelligent decisions about image-based data. The system is taught using a large volume of images, which it is trained to recognize and interpret, until it is able to recognize patterns and make decisions by itself. Then, when it is presented with new images in future, the AI algorithm knows how to process these, drawing on its prior knowledge.
AI-Powered Machine Vision Use Cases
AI-powered machine vision systems are useful in laboratory settings as part of an automation solution. Automating the lab setting means that clinicians and lab technicians have the ability to conduct tests simultaneously and record clinical data faster and more accurately and efficiently. Examples of machine vision applications in lab automation are:
- Automated Image Analysis: AI-powered systems analyze images to detect patterns and anomalies, aiding in early detection and diagnosis.
- Workflow Optimization: AI algorithms optimize sample processing workflows, ensuring faster turnaround times and better resource allocation.
- Predictive Equipment Maintenance: AI monitors lab equipment performance, predicting potential failures and scheduling maintenance to minimize downtime.
AI-based machine vision systems are popular in manufacturing settings, where they are frequently used in production line inspection to make decisions on high volumes of fast-moving items. This type of technology helps robots to understand an environment visually to make intelligent decisions, and it can even read and interpret written and numerical information via natural language processing.
AI-powered machine vision is often used for the following:
- Object detection
- Shape recognition
- Defect identification
- Automated visual inspection
- Label interpretation
- Manufacturing automation
Benefits Of Ai-Based Machine Vision
- Intelligent: AI-powered machine vision can learn on its own. This type of system is self-learning, able to train itself, and can learn from mistakes, making it highly intelligent.
- Adaptable: This type of system can quickly adapt to new data or factors and use its knowledge to make informed decisions. This makes AI machine vision ideal for fast-changing environments where flexibility is key.
- Minimal Resources Required: AI-based machine vision has a straightforward configuration, while its self-learning abilities mean it doesn’t require human expertise, helping to streamline operations and save resources.
- Highly Competent: Machine vision powered by artificial intelligence is able to work in a complex environment, while handling multiple rules or patterns, as well as variations in quality. This is a major advantage of AI vs rule-based systems.
- Scalable: AI-based machine learning can adapt quickly and seamlessly to new data or algorithms to suit evolving needs, teaching itself how to adapt to changing factors.
Rule-Based System Vs Machine Learning: Which Is Right For Your Business?
Each organization will have its own individual requirements from a machine vision system, but the below table should help you to decide whether a rule-based or AI-powered approach is best for your needs.
| Rule-Based Machine Learning | AI-Based Machine Vision | |
| Intelligence | Programmed to follow the same set of rules to make every decision. | Ability to self-learn, recognize patterns and learn from mistakes, but lacks human intuition. |
| Adaptability | Can only respond to the rules it has been programmed to follow, so requires manual input to adapt. | Highly adaptable; able to quickly adapt to new factors and teach itself how to respond. |
| Resource Intensity | Minimal input to program initial rules, but can be time and resource-intensive to re-program the rules whenever a new one is required. | AI requires intensive training, but needs minimal input once it’s set up. |
| Reliability | Highly reliable, as it follows the same set of rules each time. | Bias in the data or model can lead to unpredictability. |
| Futureproofing | Can be programmed to follow new rules, but otherwise has limited parameters. | Self-learning ability means AI machine vision can adapt to evolving requirements and scale as business needs change. |
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