Use Cases

Automated vision applications for quality assurance and NDT. Each playbook below is a worked example using a real dataset, annotation approach, AI blocks, training settings, and the pipeline that produced the result, so you can follow the same process with your own images.

Animated overview of the ReliVision platform, from data curation and training through to shop-floor inspection

One label per part

Classification

The simplest case: each image receives one label. Annotate the full image, train a classification model, and the model assigns a class to each part without identifying a specific location.

Classification of OK / NOK Bottles

Sort bottles into defected (NOK) and no-defect (OK) classes with a single classification block, using whole-image annotation.

Rotors presented for inspection, the subject of the OK / NOK rotor classification playbook

Classification of OK / NOK Rotors

The same whole-image approach applied to rotors: two classes, defected and no-defect, trained on a small balanced sample set.

Where, not just whether

Object detection

When location matters, such as finding a character, locating a scratch, or flagging an object, annotate the relevant regions with rectangles or polygons and train an object detection model. A single model can detect multiple classes in the same image.

A pseudo-coloured X-ray image of airport baggage

Detection of Threat Objects in X-ray Inspections

Detect hammers, scissors, guns and knives in pseudo-coloured X-ray baggage images for airport security — four classes, one model.

The surface of an electronic device inspected for cosmetic scratches

Detection of Cosmetic Defects on Surfaces (Electronics)

Detect scratches on router images in two classes, “scratch_normal” and “scratch_wide”, so severity comes out of the model.

An aerial photograph of solar panel arrays

Detection of Solar Panels from Aerial Photographs

Detect solar panels in aerial photographs — the same detection workflow applied to overhead imagery rather than a station.

Shape, not a box

Semantic segmentation

When the exact shape matters, such as a crack or glue smear, annotate the defect with polygons and train a segmentation model. The model traces the full defect area, making it useful when size and extent influence the inspection result.

A ceramic tile surface inspected for cracks and contamination

Detection of Defects on Tiles

Segment cracks, dye, oil and glue on ceramic tiles as a single defect class, without discriminating the type.

Two models in series

Two-stage pipelines

When you need to find something and then say what kind it is, one model does not do both well. A detector locates the region, its crop is handed to a classifier, and the two are wired together in the Pipeline Editor — no code involved.

A metal plate with an imprinted sequence of alphanumeric characters

Detection and Classification of Characters on Metal Plates

Detect the imprinted alphanumeric characters “A”, “F” and “3” on metal plates, annotated with rectangles.

Silicon wafers being inspected for defects

Detection and Classification of Defected Silicon Wafers

A detector finds each wafer, then a classifier labels it “Good”, “Small Defect” or “Big Defect” from the cropped region.

Metal nuts presented for defect inspection

Detection and Classification of Defects on Metal Nuts

A defect detector feeds a three-class classifier that decides whether the defect is bent, colour or scratch.

Build your own

Where to next

Every playbook above follows the same core workflow. The rest of the Knowledge Hub shows you how to apply it to your own parts and images.

Knowledge Hub