Computer vision – free feasibility study (Proof of Concept)

Get a reliable assessment in just a few days

In the feasibility study (Proof of Concept, PoC), we clarify the key questions before you invest time and budget into a full computer vision solution:

Result quality: Can the required measurement, detection, or classification results be achieved under realistic conditions?
Imaging setup: Which camera/lens/lighting is appropriate, and which boundary conditions are critical?
Performance: Does the processing speed (cycle time / real-time) fit your process and IT environment?

Outcome: You receive a clear recommendation including risks, edge cases, and next steps – as the basis for a sound project decision.

Computer vision: camera and image data as the basis for the feasibility study

Start right away: Contact us – we’ll tell you which sample images and requirements we need for a quick initial assessment.

What we need from you (inputs)

To make the PoC fast and meaningful, existing data is often sufficient. Ideally you can provide:

Sample images / videos (including typical variants and edge cases)
Goal definition: What needs to be detected, counted, measured, or classified? Which tolerances apply?
Boundary conditions: lighting, motion, ambient light, temperature, vibration
Process requirements: cycle time, throughput, latency, response times
IT/integration: interfaces, data storage, security requirements (e.g., network, access models)

If no data is available yet, we’ll define a pragmatic approach for data generation together (test setup / first recordings).

What you get (outputs / deliverables)

Feasibility assessment with transparent criteria (e.g., error rates, measurement deviations, stability)
Recommendation for the imaging setup (camera/lens/lighting/synchronization)
Performance assessment (CPU/GPU options, architecture, scaling)
Risk and edge-case analysis (variation, typical failure patterns, robustness)
Next steps: action plan for prototype, integration, and rollout (including effort range)

How the feasibility study (PoC) works

Initial images & first assessment: We review existing images/videos and provide an initial estimate of feasibility, risks, and data gaps.
Data check & target metrics: Together we define success criteria (e.g., error rates, tolerances) and assess variability and representativeness of the data.
Setup review: We evaluate camera/lens/lighting and synchronization; if needed, we propose concrete alternatives.
PoC implementation: We test suitable methods (classic computer vision and/or AI) and validate against the target metrics.
Performance check: We estimate runtime/throughput and evaluate options such as parallelization or GPU usage.
Result report & recommendation: Summary, risks, edge cases, and the next implementation plan.

If you want to proceed afterwards: Implementing a computer vision solution.

Checklist: typical points we verify in the PoC

Camera and lens selection: Are resolution and optics a fit? Which working distances, viewing angles, and depth of field are required?
Lighting and synchronization: uniformity, reflections, motion blur, trigger/timing.
Processing speed: Does the solution meet cycle time / real-time requirements?
Environmental conditions: temperature, vibration, contamination, changing ambient light.
Integration & data management: interfaces (e.g., DB/web services), storage, auditability, access controls.
Security requirements: transport encryption, roles/permissions, traceability.
Scalability: more cameras, more stations, higher volumes – what changes?
Training & support: onboarding, operating documentation, maintenance concept.

FAQ: computer vision feasibility study

What image quality is required?

That depends on your goal (e.g., tolerances for measurement vs. classification). Representative variants and edge cases are key. If data is missing, we’ll define a pragmatic capture plan.

Classic computer vision or AI?

We choose the approach based on the available data, robustness, and maintainability. Often, a hybrid approach works best: classic preprocessing combined with AI where variability is high.

What if it’s not feasible?

Then you’ll receive a clear explanation and concrete levers (e.g., better lighting, different optics, adjusted task definition) to reach feasibility – or a recommendation not to proceed.

What happens after the PoC?

Based on the results, we create an implementation plan (prototype → integration → rollout) and align interfaces, operations, and quality assurance.

Questions about your case? Send us a message and, if possible, include a few sample images.