Defect detection
Identify anomalies, missing parts, deformations or non-conforming features in production images.
Computer Vision for quality control
Turn a visual inspection into a measurable process.
I design Computer Vision systems to detect defects, classify objects, segment regions and analyze images or video when manual inspection is slow, variable or difficult to scale.
Applications
Detection, segmentation and classification are means. The outcome is the process.
Classification
Assign categories, states or quality levels consistently and repeatedly.
Segmentation
Measure areas, contours and regions for more precise analysis than simple presence or absence.
Data
The bottleneck is often not the model. It is the data and the definition of the defect.
Before training, we need to understand image variability, annotation quality, case distribution and what “correct” actually means in the process.
Dataset
Representative samples, edge cases, consistent annotations and a proper split between training, validation and test.
Operational metrics
Precision and recall must be translated into the real cost of errors, scrap, rework and remaining manual checks.
Deployment
Edge or cloud depending on latency, connectivity, privacy, available hardware and required throughput.
Monitoring
Performance, drift and new cases should be monitored to understand when new images or retraining are needed.
Method
Prove the signal exists first. Build the system second.
Samples
Collect and review real images.
Baseline
Run a fast test to understand whether the problem is learnable.
Validation
Compare technical metrics with operational cost.
Deployment
Integrate into the line, application or workflow.
Technical assessment
Do you already have images of defects, products or manual inspections?
An initial sample is enough to understand which questions to ask and whether a technical validation makes sense.
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