From CNNs to VLMs: The Next Leap in Industrial Inspection
Advex
Introduction: The Hidden Cost of Machine Vision Today
For decades, machine vision in manufacturing has been dominated by Cognex, Keyence, and CNN-based systems. These tools have served industry well due to their fast cycle times and widespread support. However, anyone who’s managed them knows the hidden costs.
Every time conditions change, perhaps a new SKU, a lighting shift, or supplier variation, systems must be reprogrammed. That usually means waiting for an external sales engineer to drive in or stealing away expensive controls engineers from higher-value tasks. Either way, machine vision projects drag on for weeks or months, ROI erodes, and many automation initiatives stall out before they reach production.
Now, a new class of AI is changing what’s possible: Vision-Language Models (VLMs). These larger, more capable AI models can handle complex, variable tasks and they’re starting to reshape industrial inspection.
What Are VLMs, and Why They Matter for Industrial Automation
At their core, VLMs are large AI models trained on billions of image–text pairs. Unlike CNNs, which are much smaller, VLMs arrive with a broad “visual vocabulary.” Crucially, VLMs don’t just see pixels, they also connect those visuals to language.
This makes them uniquely powerful for inspection tasks:
They can understand both the image of a scratch and the concept of “scratch.”
They adapt to new tasks with just a handful of labeled examples, not hundreds.
They bring the latest advances from companies like OpenAI (GPT5), Meta (Llama), Google (Gemini), and Alibaba (Qwen) into the factory setting.
For manufacturers, the impact is direct:
Faster ROI: Instead of waiting weeks or months to collect hundreds of defect images, teams can hit accuracy targets in days, sometimes in a single shift.
Reduced maintenance costs: Updates don’t require scarce controls engineers; operators can handle re-training in minutes.
Scalability: One model setup can be copied across lines and sites, adapting with just a handful of new images.
The Data Efficiency Advantage
The data gap is where VLMs really shine. Traditional CNNs need 80–150 examples per defect type to reach usable accuracy. VLMs get there with fewer than 15.
That’s the difference between:
Months of data collection vs. a morning’s work.
AI projects stuck in R&D vs. running on the line.
Downtime and missed ROI vs. production-ready deployment.
In other words, VLMs don’t just improve accuracy. They unlock the business case that’s kept AI vision from scaling in manufacturing for decades.
VLMs vs CNNs: A Data and Speed Revolution
The difference shows up clearly in practice — and it’s not subtle.
In a rim scratch detection benchmark:
A CNN-based system (ResNet-34) needed 98 images to reach ~80% accuracy. Setup took ~90 minutes and required a controls engineer.
Advex Composer (VLM-based) achieved 94% accuracy with just 9 images, and setup took only 5 minutes — done by an operator with no AI expertise.
See the full breakdown and testing methodology here.
This is more than a technical win. It’s a business breakthrough:
Data burden: Collecting and labeling 98 defect images typically takes 3–4 weeks of production time. Nine images can be gathered in a single shift.
Setup cost: A controls engineer’s time is scarce and expensive; CNNs consume it. VLMs free that time up for higher-value projects.
Scaling: A single factory line can easily need 500+ new defect examples per year to keep CNNs accurate as SKUs, lighting, and suppliers change. VLMs cut that by an order of magnitude.
Side-by-Side Comparison
System | Images Needed | Accuracy | Setup Time | Who Can Do It |
CNN (ResNet-34) | 98 | ~80% | 90 min | Controls Engineer |
VLM (Advex Composer) | 9 | 94% | 5 min | Operator (no AI exp.) |
Why the gap?
CNNs are narrow: they only learn from the dataset provided, making them brittle with small or shifting data.
VLMs are broad: pretrained on billions of image–text pairs, they combine visual and language cues to specialize instantly with a handful of examples.
The result is consistent across defect types: 5–15 images for VLMs to match or beat CNN accuracy that requires 80–150 images. That’s a 10x reduction in data requirements, 18x faster setup, and higher accuracy.
In short: CNNs are scalpels. VLMs are Swiss Army knives: broader, more adaptable, and ready to scale.
VLMs vs Industry Standards: Cognex and Keyence
Cognex and Keyence remain the backbone of industrial inspection. They’re integrated, proven, and reliable in stable environments. But they come with three weaknesses:
Reprogramming burden: Drift or variation requires expert intervention.
Scaling challenges: A system that works on Line A often can’t be copied to Line B without weeks of rework.
Downtime costs: Relying on external or scarce engineers creates delays and dependency.
VLMs flip this equation:
They can adapt to new defects and parts with just a handful of examples.
They empower operators to set up tasks in minutes without ML expertise.
They lower the total cost of ownership by reducing reliance on external vendors to reduce downtimes.
The Hard Truth: VLMs Still Have Challenges
As promising as they are, standard VLMs aren’t production-ready out of the box.
Latency: Large VLMs take several seconds per image, far too slow for inline inspection.
Domain mismatch: Factory-specific defects are often very different from the images VLMs are commonly trained on.
Deployment issues: Most VLMs are cloud APIs, raising uptime risks, IP leakage, and compliance concerns.
Closing the Gap: Making VLMs Industrial-Ready
This is where Advex steps in. We’ve focused on solving the gaps that hold VLMs back:
Speed: Optimized inference achieves <500ms inference times at the edge.
Data scarcity: Synthetic data generation fills in rare and edge-case defects.
Ease of use: Advex is no-code; operators can train and update in minutes.
Security & reliability: Advex VLMs run locally at the edge, not in the cloud, eliminating compliance and uptime concerns.
Conclusion: From Lab Promise to Production Reality
VLMs are the future of industrial inspection. They unlock ROI by slashing data needs, reducing maintenance costs, and scaling across lines. But without solving speed, domain, and deployment issues, they stay stuck in the lab.
At Advex, we’ve built Composer to close those gaps — delivering production-ready VLMs for the factory floor.
👉 Want to see the difference yourself? Upload a handful of defect images, and in just five minutes, you’ll have a working inspection system ready to run.