# sups_yolo

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

## Goal

Detect and classify defects on polyurethane soles in several categories, despite moderate disturbances such as dust, glare, and varying lighting conditions.

## System overview

- **Vision hardware**: 2–3 Raspberry Pi or IP cameras with web access + a workstation with an RTX 2060; Full HD cameras.
- **Software**: Linux-like OS, logging of processed data, YOLO-based detection instances per camera.
- **User web interface**: history view, validation status, expert feedback (correct/incorrect), multi-channel tabs (camera 1/2/3 with independent YOLO instances), live camera preview for setup, settings section, retraining with date-restricted data.
- **Event record per sole**: sole ID, defect photo, defect probability, annotated photo with defect zone.
- **Performance target**: 15 seconds per image analysis and result description.

## Documentation

Project documentation lives in [`docs/`](docs/).