Computer vision for quality inspection is one of the most proven AI applications in manufacturing. But “proven” doesn’t mean “easy.” I’ve seen too many projects that looked great in demos but struggled in production.

Here’s a practical guide based on implementations that actually worked.

Phase 1: Define the problem precisely

Before buying anything, get crystal clear on what you’re trying to achieve.

What defects are you trying to catch?

Be specific. “Visual defects” is too vague. List the actual defect types:

• Scratches deeper than X mm
• Colour variation outside Y tolerance
• Foreign material contamination
• Dimensional deviation from specification
• Surface irregularities of type Z

For each defect type, understand:

• How does it appear visually?
• How frequently does it occur?
• What’s the cost when it escapes detection?
• How reliably can humans detect it currently?

Where in the process should inspection happen?

Earlier detection means less wasted value-add. But earlier in the process, products might be harder to inspect (hot, dirty, moving fast).

Map your current inspection points. Where do defects get caught? Where do they escape? Where would earlier detection provide the most value?

What happens when a defect is detected?

Rejection systems need to integrate with the vision system. Options include:

• Automatic physical rejection
• Operator alert for manual removal
• Diversion to rework
• Line stop for critical defects

The rejection mechanism often costs more than the vision system itself. Plan for it early.

Phase 2: Assess feasibility

Not every inspection problem is solvable with current technology. Before committing, assess feasibility.

Image capture challenges

Can you physically capture usable images of the inspection area?

Consider:

• Line speed vs exposure time
• Lighting conditions (ambient variation, reflective surfaces)
• Mechanical stability (vibration causing blur)
• Field of view requirements
• Resolution needed to see target defects

If you can’t get good images, AI won’t help.

Defect visibility

Can a human see the defect in the captured image? If an experienced quality person can’t identify the defect from a still image, don’t expect AI to do better.

Get sample images of good and bad products. Can you tell them apart? If not, work on the imaging before worrying about AI.

Sample availability

AI training requires examples of both good products and defects. Rare defects are a challenge—you might not have enough examples to train a reliable model.

For a typical implementation, you want:

• Hundreds to thousands of examples of each defect type
• Much more good product examples
• Representative variety (different lighting, positions, product variants)

If defects are extremely rare, you might need to artificially create examples or use different approaches (anomaly detection rather than classification).

Phase 3: Pilot design

Start small. A well-designed pilot teaches you what you need for full implementation.

Single inspection point

Pick one location for the pilot:

• High defect volume (so you see enough defects to evaluate performance)
• Accessible for iteration (can adjust cameras, lighting, positioning)
• Representative of broader rollout challenges

Minimal viable system

For the pilot, you need:

• Camera and lens appropriate for the application
• Lighting (often the most underestimated element)
• Processing hardware (industrial PC or edge device)
• Vision software (vendor platform or custom)
• Integration to existing systems for data and possibly rejection

This might cost $30,000-80,000 depending on complexity. Worth it to learn before committing to millions.

Success criteria

Define upfront what success looks like:

• Detection rate (% of defects caught)
• False positive rate (% of good product rejected)
• Throughput (can it keep up with line speed)
• Reliability (uptime, stability)

Be realistic. 99% detection with 0.1% false positives is achievable for some applications, not others. Set targets based on what’s needed to justify the investment.

Phase 4: Implementation

Lighting is critical

I can’t emphasise this enough. Poor lighting causes more vision system failures than poor AI. Work with optics experts to get lighting right:

• Consistent illumination across the field of view
• Appropriate for the defect type (backlighting, ring lights, diffuse lighting, structured light)
• Minimised ambient light interference
• Robust to environmental changes

Budget significant time for lighting trials. What looks fine to human eyes often doesn’t work for machine vision.

Camera and optics selection

Choose based on:

• Resolution needed (determined by smallest defect size)
• Field of view (area to inspect)
• Line speed (affects shutter speed requirements)
• Colour vs monochrome (colour needed for some defects, mono often better for others)
• Environmental protection (IP rating for your environment)

Industrial-grade cameras from companies like Cognex, Keyence, Basler, or FLIR are standard. Consumer cameras don’t survive factory environments.

Software platform

Options include:

Vendor integrated solutions: Cognex VisionPro, Keyence CV-X, SICK Inspector. Easier to deploy, less flexibility, vendor lock-in.

Open frameworks: OpenCV, TensorFlow, PyTorch with industrial interfaces. Maximum flexibility, requires development expertise.

Specialised AI platforms: Landing AI, Neurala, etc. Balance of capability and ease of use.

For most mid-size manufacturers, vendor integrated solutions make sense. Custom development requires ongoing maintenance that’s often underestimated.

Training the model

For AI-based systems:

1. Collect labelled images (defect type, location)
2. Split into training and validation sets
3. Train initial model
4. Test on held-out data
5. Review failures and false positives
6. Collect additional examples for weak areas
7. Retrain and iterate

This isn’t a one-time activity. As products change and new defect types appear, models need updates.

Integration

Connect the vision system to:

• PLC/control system (for rejection triggering)
• MES/quality system (for logging and traceability)
• Alert systems (for operator notification)
• Data storage (for image archives)

Test integration thoroughly. A vision system that detects defects but can’t trigger rejection is useless.

Phase 5: Validation and deployment

Validation testing

Before going live, validate systematically:

• Run known defects through the system (do they get caught?)
• Run good product through (are false positives acceptable?)
• Run for extended periods (is performance stable?)
• Test edge cases (shift changes, product variants, lighting changes)

Document everything. This is your evidence that the system works.

Operator training

People need to understand:

• What the system does and doesn’t do
• How to respond to alerts
• What to do when the system flags questionable items
• How to handle system failures
• Basic troubleshooting

Rollout

Go live with appropriate support:

• Vendor or integrator on site initially
• Parallel running with existing inspection (if possible)
• Close monitoring of performance
• Rapid response to issues

The first few weeks typically require significant attention. Plan for it.

Phase 6: Ongoing operations

Performance monitoring

Track continuously:

• Detection rate (requires auditing escapes)
• False positive rate (rejected good product)
• System uptime
• Processing latency

Drift happens. Performance that was great initially can degrade as products, lighting, or equipment changes.

Model maintenance

Plan for regular model updates as:

• New products are introduced
• New defect types emerge
• Performance drifts

This requires ongoing capability, either internal or contracted.

System maintenance

Cameras get dirty. Lighting ages. Hardware fails. Include vision systems in your maintenance program.

Common failure modes

• Lighting changes: Ambient conditions affect results. Need robust lighting design.
• Product variation: Models trained on one product variant fail on others.
• Defect evolution: New defect types appear that weren’t in training data.
• Integration failures: System detects defects but rejection doesn’t work.
• Operator workarounds: People bypass the system because of false positives.

Anticipate these and plan for them.

When to get help

Computer vision implementation is genuinely technical. Most manufacturers don’t have this expertise in-house.

Options include:

• Vision system vendors (Cognex, Keyence, etc. often do implementation)
• Industrial system integrators with vision expertise
• Specialised AI companies (including AI consultants Melbourne for custom requirements)

Choose partners with actual industrial experience, not just AI expertise. Factory environments are different from labs.

Computer vision for quality inspection works. But it works because of careful implementation, not magic. Do the work upfront, and you’ll get a system that delivers value for years.