Clarify the inspection task and data
Define target features, constraints, available images, known failure modes and success criteria.
Industrial machine vision with Python
Structured support from available image data to a traceable evaluation – with explicit assumptions, measurable criteria and documented limits.
Machine vision consulting
Turning image data into defensible engineering insight
An industrial inspection task is never determined by the algorithm alone. Image acquisition, illumination, perspective, optics, calibration, data quality and evaluation criteria jointly influence the result. I help make these dependencies explicit and build analysis workflows whose results remain reviewable.
I combine practical experience in industrial machine vision with Python at ZEISS in Jena with systems engineering, technical data analysis and my current work with knowledge graphs. This supports not only image evaluation but also structured documentation of requirements, assumptions, decisions and open validation points.
Possible project support
Structure image data with Python
Load datasets, assess quality and distribution, organise metadata and prepare reproducible analysis workflows.
Build OpenCV prototypes
Prototype and compare preprocessing, segmentation, feature extraction or geometric analysis where suitable for the task.
Analyse errors and edge cases
Investigate misclassifications, unstable parameters and systematic deviations using defined examples and metrics.
Prepare validation
Document test cases, measurements, acceptance criteria and missing evidence so the next test stage is explicit.
Hand over traceable results
Present code, analyses, assumptions, known limits and recommendations clearly for the intended audience.
Evidence
A prototype is not an industrial release
An evaluation on example images can show whether an approach is fundamentally promising. It does not prove stable performance under production conditions. Defensible industrial assessment requires representative real images, the intended target hardware, controlled acquisition conditions, calibrated settings and predefined acceptance criteria.
This boundary is documented explicitly. Unsupported assumptions are not presented as confirmed results. That reduces later misunderstandings and makes the required follow-up tests visible.
Possible deliverables
- a reproducible Python notebook or clearly structured analysis code,
- a concise description of dataset, inspection features and constraints,
- a comparison of suitable method variants using transparent criteria,
- error analysis with concrete examples rather than only a global metric,
- documentation of open gaps and a specific plan for further validation.
The useful result depends on the project stage. It may be an early feasibility analysis, a focused prototype, a structured evaluation of existing tests or preparation for later technical validation.
Common questions
Can existing image data be used?
Yes. An initial assessment requires context on acquisition conditions, classes, failure modes and the desired result. Confidential data should only be provided through an agreed, suitable transfer route.
Can the work be done remotely?
Task clarification, data analysis, result reviews and documentation can generally be handled digitally. Whether work on the physical system is required depends on the inspection task and project stage.
Which methods will be used?
That decision follows from the images and requirements. A simple, reviewable method is often more useful than a complex model without a sufficient evidence base.
How are decisions documented?
Methods, parameters, sources, assumptions, test results and open gaps are connected. The article on knowledge graphs in industrial machine vision explains the approach.
Start with a concrete inspection task
- Describe the starting point: inspection feature, available images, known issues and desired result.
- Define a feasible scope: delimit data access, output, validation level and timeframe.
- Deliver a traceable analysis: review intermediate results, document limitations and derive next steps.