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sups_yolo / docs / project_context.md
@Eugene Sukhodolskiy Eugene Sukhodolskiy 3 days ago 2 KB docs: add AGENTS.md, README, and architecture docs
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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.