Rule-Based vs. AI-Based Machine Vision

There are two primary approaches to machine vision: Rule-based and AI-based 

Rule-based machine vision is a traditional approach that uses predefined rules or algorithms to identify and classify objects in an image. This involves writing code or creating a flowchart that specifies the exact steps the software should take to process an image and make a decision based on the visual information. The ‘rules’ are created based on the desired outcome, and the software follows these rules to classify objects and make decisions. 

Pros:  

1. Straightforward and easy to understand. 

2. Highly predictable and reliable. The software follows the same set of rules every time. 

3. Well suited for applications that require high-speed processing and low latency.

Cons:  

1. Not very flexible and requires manual effort to make changes. 

2. Difficult to create rules that can handle all possible scenarios, especially in complex and dynamic environments. 

3. Not very effective in handling variations in image quality or objects that are similar but not exactly the same. 

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AI-based machine vision, also known as deep learning or machine learning, is a more recent approach to machine vision that uses artificial neural networks to automatically learn how to identify and classify objects in an image. The software is trained on a large dataset of images, and learns how to recognize objects by adjusting internal parameters. The software uses this knowledge to make decisions based on new images that it has never seen before.  

Pros:  

1. Highly flexible and can automatically adapt to changing environments. 

2. Handle variations in image quality and objects that are similar but not exactly the same. 

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3. Very effective in complex and dynamic environments.

Cons:  

1. The training process can be time-consuming and requires a large dataset of images.

2. Results can be unpredictable because the software is making decisions based on the information it has learned, rather than following a set of predefined rules. 

3. The software may not always make the correct decisions, especially if the training data is biased or the model is overfitting the data. 

Both approaches have their strengths and weaknesses, and the choice of approach depends on the specific requirements of the application. 

AI-based vision is opening up the door for applications that would have been impossible just a few years ago. Flaw detection (quality inspection) is particularly well suited for an AI based vision approach. 

BlueBay is currently working with a manufacturer on a quality inspection process to detect more than a dozen flaws in glass containers as they move rapidly down a conveyor. This process is currently all manual, extremely time consuming, and somewhat subjective from operator to operator. 

By implementing an AI-based vision approach, we can teach a database with thousands of ‘good’ and ‘bad’ images that allows the algorithm to quickly analyze large amounts of data and detect patterns that may indicate defects. 

The major benefits of this approach are improved product quality and increased efficiency in the manufacturing process. For these reasons, many AI based vision solutions have an extremely fast ROI. 

At BlueBay Automation we represent AI vision solutions from SensoPart, LMI Technologies, Asyril, Neurala, and more. Reach out to us if you have a vision application where you would like to evaluate an AI based vision approach.   https://bluebay-automation.odoo.com/contact-our-team-today