Inspection System Merges Classical Image Processing with AI in Modern Machine Vision
For decades, machine vision systems have relied on classical image processing algorithms, deterministic, rule-based, and engineered for precision. These systems excel at performing specific visual tasks under controlled conditions: measuring dimensions, detecting edges, verifying presence, and more. However, they often struggle with variability in lighting, object appearance, or assembly inconsistency that occurs in real-world industrial environments.
In recent years, Artificial Intelligence (AI), particularly Deep Learning, has dramatically reshaped the landscape. Instead of rigid rule sets, AI systems learn directly from data, recognizing patterns and anomalies that are difficult to define explicitly. This shift has opened the door to new capabilities in visual inspection, classification, segmentation, and semantic understanding of scenes.
But the real power lies not in replacing classical methods — rather, in combining them.
A Synergistic Approach
A hybrid machine vision system leverages the best of both worlds:
- Classical image processing provides fast, physics-based analysis and high control over image pre-processing.
- AI models offer flexibility, adaptive learning, and robust handling of real-world variability.
For example, traditional algorithms may be used to locate regions of interest, align images, or enhance contrast. AI then steps in to interpret complex patterns such as verifying whether a connector is properly seated, detecting subtle defects, or confirming semantic correctness in assemblies.
Industrial Benefits of Integration
- Higher inspection accuracy in unpredictable environments.
- Reduced false positives/negatives through better contextual understanding.
- Faster deployment with fewer hard-coded rules and more adaptive logic.
- Improved maintainability — less time spent tuning thresholds or filters.
This combined approach is especially valuable in high-mix, low-volume manufacturing, where product variability is high, and customization is key.
Use Case: Real-Time Assembly Verification
A practical example comes from the latest product: RUBY-AI from Brossh, an AI-powered optical head mounted on a robotic arm. It autonomously scans complex assemblies, verifying component presence, position, orientation, and detecting defects all in real time. By fusing AI-based decision-making with classical image pre-processing, the system achieves robust results even under dynamic factory conditions.
Looking Ahead
The future of machine vision is hybrid, intelligent, and collaborative. Engineers, AI specialists, and system integrators must work together to craft solutions that are not only technically sound but also commercially viable and scalable.
AI does not replace classical vision. It enhances it making it more adaptive, more insightful, and ultimately, more powerful.
Author: Michael Geffen is an industry veteran and pioneer in machine vision and automated inspection systems and founder of multiple vision-tech companies.
For more information: www.brossh.com
