Surface and component inspection
Check defined defect patterns on products and surfaces using reproducible images.
Industrial computer vision
Computer vision for mid-sized manufacturers
Manual visual checks can be difficult to scale consistently across products, lighting conditions and defects. We define image capture, labeling, evaluation and operator review before integrating vision models into inspection workflows.
Where the current process reaches its limits
Start with the business workflow.
Repeated visual checks can become bottlenecks when throughput increases, shifts change or defects are difficult to recognize. Manual inspection logs are often disconnected from orders, batches and machines.
Applications and business capabilities
The right use case depends on available data, operational value and verification requirements.
Assembly completeness
Verify whether required components are present and correctly positioned.
Packaging and label inspection
Detect missing labels, incorrect codes or packaging defects.
Counting and classification
Count objects or classify known variants in controlled image conditions.
Document operational anomalies
Link visual findings to process steps, images and production data.
Support inspection teams
Route uncertain or critical findings to operators instead of replacing accountable checks.
How the technology works in a real system
Industrial computer vision begins before the model: camera placement, lighting and triggers determine image quality. Pre-processing, classical algorithms or trained models produce scores; thresholds and business rules determine whether an operator or system receives the result. Integration connects the finding to a part, line and manufacturing order.
From requirements to a verified implementation
We validate the assumptions and practical workflow before expanding implementation scope.
Define inspection objective and defect classes
Specify which errors matter, their consequences and acceptable review steps.
Collect realistic image data
Capture good and defective items across variants, lighting and real production conditions.
Compare feasibility and baseline
Evaluate conventional image processing and ML for relevant errors and throughput.
Develop the system and interfaces
Connect camera capture, results, data storage and production applications.
Pilot and monitor inspection quality
Measure missed defects, false alarms, throughput and changes over time.
When the investment makes technical and business sense
Computer vision is economically relevant where inspection volume or failure consequences justify camera setup, annotation, integration and maintenance. A model's average accuracy is not enough: measure missed defects and false alerts by class, and determine when human review is required.
Requirements for reliable delivery
These factors define feasibility, evaluation effort and ongoing responsibility.
Reproducible imaging
Control light, focus, position and camera triggers.
Representative defect examples
Include rare faults, component variants and realistic negatives.
Production integration
Connect decisions to part identity, operator workflow and ERP or MES.
Transparent quality metrics
Evaluate false negatives, false positives and operational throughput.
Operational change control
Retest after changes to product, camera, model or process.
Engineering the complete capability
We engineer computer vision as a complete operational system rather than an isolated model demonstration. Camera setup, tests, software integration and human review are all part of the implementation. Published case results are not implied by these illustrative use cases.
Frequently asked questions: Computer vision for mid-sized businesses
Practical questions about feasibility, integration, cost and quality.
Which mid-sized manufacturers can use computer vision?
Those with a clearly defined inspection task, meaningful error costs and suitable imaging conditions.
Do we need large training datasets?
Not always. Data needs depend on the defect, variation and whether conventional vision methods suffice.
Can it replace manual inspection completely?
Only when the process, risks and test results justify that decision. Many systems retain human review for uncertain findings.
How do we connect findings to ERP or MES?
Through stable identifiers, event APIs and explicit failure handling.
How do we measure value?
Compare inspection performance, false alarms, missed faults, time and operating cost against the current process.
Is cloud infrastructure required?
No. Deployment can be local or cloud-based depending on latency, connectivity and data constraints.
Discuss your project
What would you like to improve?
Describe the current workflow, systems, available data and intended result. Aleksandar Basara can discuss a technically grounded starting point.
Aleksandar Basara
Managing Director
aleks@valanor.co
+49 (0) 711 16 44 6 83
Lindenspürstrasse 22, 70176 Stuttgart
