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/.