Checklist for machine vision projects
Important questions before starting a project
Many machine vision projects do not fail because of the actual evaluation, but because of unclear boundary conditions. Anyone who clearly defines early on what is to be detected, measured, inspected or documented saves time, cost and unnecessary iterations later. This checklist helps prepare a machine vision project in a structured way – regardless of whether it is about quality inspection, measurement systems, color inspection or 3D machine vision.
The questions are intentionally phrased in a practical way. They help make technical risks visible, improve communication between domain experts and development, and create a reliable basis for a feasibility study or direct implementation.
1. Describe the task and objective clearly
What exactly should the solution do?
- What should be detected, measured, counted, classified or assessed?
- Which features are truly relevant for the decision?
- Is there a clear pass/fail criterion or multiple classes?
- What final result is expected: numeric value, approval, report, image marking?
Why is this important?
The more precisely the task is described, the easier it is to decide whether classical machine vision, AI or a combination of both makes sense. Unclear goal definitions almost always lead to unnecessary loops in the project.
2. Define quality criteria and tolerances
A system can only work reliably if it is known what accuracy, which tolerances and which defect types are relevant. This applies to measurement tasks as well as optical inspections. What matters is not only the ideal state, but also the definition of borderline cases and acceptable deviations.
Especially in quality-inspection projects, it is crucial whether the smallest deviations must be detected, whether statistical variations are acceptable or whether robust classification is sufficient. These points directly influence camera selection, resolution, lighting and the appropriate evaluation approach.
Typical questions
- What accuracy is needed for the task?
- Which deviations are acceptable and which are not?
- What defect patterns occur, and how often?
- Are there reference samples for good and bad parts?
3. Evaluate data and sample material
Existing data
- Are there already sample images, videos, measurement data or specimens?
- Are good and bad cases documented sufficiently?
- Do the data cover variants, disturbances and borderline cases?
- Are there data gaps that should be closed before the project starts?
Practical significance
A realistic data basis is often the fastest way to a reliable assessment. Especially in AI projects, the quality and variety of the data determine whether a solution will later work robustly. But classical methods also benefit greatly from good test data and clean examples.
4. Understand image capture conditions
Machine vision does not begin with the algorithm, but with image capture. That is why camera, optics, lighting, distance, perspective, background, motion and ambient light should be considered early. Even small differences in image capture can determine whether an evaluation works stably or constantly fails on exceptions.
If there are still uncertainties here, that is not a problem – but it should be stated openly in the project. In many cases, this directly results in the need for test recordings, a lighting trial or an early technical assessment of the hardware.
Important points for image capture
- Which camera is available or planned?
- Is the object stationary or moving?
- Are there reflections, shadows or fluctuating ambient light?
- How large is the relevant field of view?
- Is defined lighting available?
- What role do focus, perspective and depth of field play?
- Is 2D or 3D data required?
- Are camera, optics and lighting chosen appropriately?
5. Consider process, integration and operation
A good machine vision solution is not only technically correct, but also cleanly integrated into the workflow. That is why it should be clarified early where the solution will be used, who will operate it, which data will be stored and which interfaces must be considered. This applies to new projects just as much as to retrofit initiatives.
Questions about integration
- At what point in the process does the evaluation take place?
- What cycle time must be met?
- Do machines, PLCs, databases or web services need to be connected?
- Who sees the results, and in what form?
Questions about operation
- Which results should be logged or archived?
- How are error cases or uncertain decisions handled?
- How important are maintainability and later expandability?
- Should the system run locally, on the network or as a web solution?
Typical stumbling blocks in machine vision projects
- The task is only described roughly and not defined using clear criteria.
- Only ideal sample images are available, but no realistic borderline cases.
- Lighting and image-capture conditions are considered far too late.
- Integration into the process is underestimated or only planned afterward.
- There is no clear idea of how results should be documented and evaluated.
These are exactly the kinds of issues that can often be avoided with proper preparation. That is why a structured checklist is not a formal add-on, but a practical tool for better decisions.
When the checklist should become a feasibility study
If, after the first review, there is still uncertainty about the data situation, image capture, accuracy or solution approach, a feasibility study is usually the best next step. It translates open questions into a reliable technical assessment and reduces the risk of investing directly in an unsuitable implementation.
If the requirements are already clear and good sample material is available, the implementation can be planned directly on that basis. In both cases, the checklist is useful: as a basis for decisions and as a starting point for the project.
Frequently asked questions about the checklist for machine vision projects
For which projects is this checklist useful?
For almost all projects involving camera and software-based evaluation – from quality inspection and measurement systems to color inspection, 3D evaluation or AI-supported classification.
Is the checklist enough to start a project?
It is a very good starting point, but it does not replace a feasibility study in every case. If important points are still open or technically risky, a reliable assessment should be carried out before implementation.
What if not all questions can be answered yet?
That is normal. This is exactly where the checklist helps: it shows which points are still open and where targeted data, test recordings or technical clarification are required.
Is the checklist also relevant for existing systems?
Yes. It also helps with the extension or modernization of existing inspection stations by systematically capturing boundary conditions, interfaces and improvement potential.