# Project context

## Theme

Development of an intelligent information-measuring system for real-time control of geometric and physico-mechanical parameters of polyurethane shoe soles.

## Goal

Determine and classify (into several categories) the presence of defects on a shoe sole despite moderate disturbances such as dust, glare, and varying lighting.

## Functional requirements

### User web interface

- Display inspection history on screen.
- Show status: validated.
- Expert can mark the recognition result as correct or incorrect.
- Omnichannel support: tabs 1, 2, 3 for different cameras / Raspberry Pi instances, each with its own YOLO instance and settings.
- Display camera image for initial setup.
- Settings section.
- Retraining function with date restriction.

### Machine vision system

- Hardware:
  - 2–3 Raspberry Pi devices or IP cameras with web access.
  - Workstation with RTX 2060 GPU.
  - Full HD cameras.
  - Investigate recognition-quality change when replacing the camera with another model.

- Software:
  - Linux-like operating system.
  - Log of processed data.
  - Event record per inspected sole:
    - sole number;
    - defect photo;
    - probability score for defect presence;
    - separate annotated photo marking the defect zone.
  - 15-second budget for image analysis and description output.

## Operating conditions

- Describe working conditions: lighting level, type and intensity of disturbances, and estimated probability of influence on the result.
- Determine optimal positioning of the sole relative to the camera.
- Allowable part position: rotation ±5° relative to the camera, displacement up to 10% of the frame.

## Testing and validation

- Test and debug the system under conditions close to production (at home/lab environment).
- Noise generator for dust emulation.
- Separate photo set not used during model training:
  - 20 pieces without defects;
  - 3 pieces with defects.
- Photo effects to simulate different lighting conditions.
- Overlay PNG patterns to simulate lens contamination and other obstacles.
- Photo rotation to simulate positioning deviations.
- Testing is performed after a fixed time from the casting process, when the geometric dimensions have stabilized and no longer change.

## Retraining

- Retrain the model using data collected during operation.
- The operator acts as an expert and verifies the model result.