Architecture components

This file lists each component from the architecture diagram in detail. Use it as a reference when implementing or modifying a module.

Per-camera pipeline

IP Camera / Raspberry Pi

• Role: image acquisition.
• Expected count: 2–3 channels.
• Output: continuous video stream.
• Notes:
  • Cameras may be replaced; recognition quality impact must be measurable.
  • Positioning tolerance: rotation ±5°, displacement up to 10% of frame.

Position determination

• Role: decide when to capture a frame.
• Options:
  • Physical sensor on conveyor (simpler, deterministic);
  • AI-based detector (more flexible, needs training).
• Output: trigger signal to frame extractor.

Video stream → frame

• Role: extract still frames from the camera stream on demand.
• Input: trigger signal.
• Output: raw image frame.

Image preparing

• Role: make the image suitable for the YOLO model.
• Operations:
  • normalize pixel values / brightness / contrast;
  • filter noise;
  • crop to region of interest;
  • resize to model input size;
  • rotate to compensate for small alignment errors.
• Must be configurable per camera channel via JSON config.

Inference layer

YOLO instance

• Role: run defect detection on a prepared image.
• One instance per camera / tab.
• Each instance has:
  • own model weights;
  • own confidence threshold;
  • own class mapping;
  • own preprocessing parameters.

AI Model

• Role: manage model artifacts and retraining lifecycle.
• Responsibilities:
  • load weights for YOLO instances;
  • export updated weights after retraining;
  • keep versioned model history;
  • serve as an abstraction between training pipeline and inference.

Central services

Main Server

• Role: coordination and API.
• Responsibilities:
  • read JSON config at startup;
  • manage camera channels;
  • dispatch prepared images to correct YOLO instance;
  • collect detection results;
  • persist events to DB;
  • expose HTTP/WebSocket API for WEB UI;
  • handle retraining requests.

Database (DB)

• Role: persistent storage.
• Stores:
  • inspection events (sole ID, timestamp, channel, result);
  • original images;
  • annotated images with bounding boxes;
  • expert verification labels;
  • model versions;
  • configuration snapshots.

User interface

WEB UI

• Role: human operator and expert interface.
• Views:
  • history list with filtering;
  • detail view: original image, annotated image, probability, status;
  • expert feedback buttons: correct / incorrect;
  • multi-channel tabs (1, 2, 3) with per-channel YOLO settings;
  • live camera preview for mechanical setup;
  • settings form;
  • retraining panel with date-range restriction.

Development / testing components

Fake input data (factory environment emulation)

• Role: offline input generator.
• Used when cameras are not connected.
• Produces synthetic frames or replays recorded frames.

Artificial image generator

• Role: augment datasets to simulate factory disturbances.
• Applied to:
  • initial dataset;
  • learning dataset.
• Transformations:
  • lighting effects;
  • PNG pattern overlays (dust, dirt, lens contamination);
  • rotation (±5°);
  • other noise.

Learning Dataset

• Role: curated data used to train / retrain the model.
• Sources:
  • initial dataset;
  • artificial generator output;
  • verified production data from expert feedback.

Learning / Training module

• Role: run model training and retraining.
• Inputs:
  • Learning Dataset;
  • configuration (hyperparameters, date range).
• Outputs:
  • new model weights;
  • training metrics;
  • updated AI Model entry.