Rule-Based vs. AI-Based Machine Vision

Machine vision technology is evolving rapidly, and there are two primary approaches that stand out: Rule-based and AI-based.

Rule-Based Machine Vision: Traditional and Algorithmic

Rule-based machine vision is a time-honored approach, relying on predefined rules or algorithms to identify and classify objects in images. This method involves programming specific steps for the software to process an image and make decisions based on visual information. The rules, tailored to desired outcomes, guide the software in object classification and decision-making.

Pros of Rule-Based Vision:

• Clear and easy to comprehend.

• Highly predictable, following the same rules consistently.

• Ideal for high-speed processing with low latency.

Cons of Rule-Based Vision:

• Limited flexibility; manual adjustments required for changes.
• Struggles in complex, dynamic environments and with varying image quality.
• Limited effectiveness with objects that are similar but not identical.

AI-Based Machine Vision: Learning and Adapting

On the other hand, AI-based machine vision, encompassing deep learning and machine learning, leverages artificial neural networks. These networks are trained on extensive image datasets, learning to recognize objects by adjusting internal parameters. This process enables the software to make informed decisions on new, unseen images.

Pros of AI-Based Vision:

• Adaptable to changes and varying environments.
• Handles image quality variations and similar objects effectively.
• Excels in complex, dynamic settings.

Cons of AI-Based Vision:

• Time-intensive training process requiring extensive datasets.
• Unpredictable outcomes, as decisions are based on learned data.
• Potential for incorrect decisions due to biased training data or model overfitting.

The choice between these two approaches depends on the specific needs of the application. AI-based vision is particularly transformative for tasks like flaw detection in quality inspection, which were challenging a few years ago.

For instance, BlueBay is collaborating with a manufacturer to automate the quality inspection of glass containers. This process, initially manual and time-consuming, varies between operators. By implementing AI-based vision, we utilize a database with thousands of 'good' and 'bad' images, enabling the algorithm to analyze large data volumes and identify defect patterns quickly.

The advantages of AI-based vision include enhanced product quality and manufacturing efficiency, often leading to a rapid return on investment. At BlueBay Automation, we offer AI vision solutions from brands like SensoPart, LMI Technologies, Asyril, Neurala, and more. If you're considering an AI-based vision approach for your vision application, reach out to our team for an application assessment.