Machine Vision PoC

Explore camera-based inspection of production processes using reproducible illumination and machine vision. Build a reviewable defect catalogue and a small operator interface as the foundation for later software development.

First use case: surface inspection of Carbofol sealing membrane. The requirements, hardware estimates and experiments below describe this initial use case; future materials and processes may require different capture configurations, models and acceptance criteria.

Status: documentation and source placeholder only. No capture service, trained model, benchmark, validated detection accuracy or deployed application exists in this repository. Hardware has not been selected or purchased as part of this task.

Initial use case: requirements from the discussion

Constraint	Current scope
Material	Carbofol membrane; exact product and surface variant to confirm
Web speed	Maximum 4 m/min
Observed width	Approximately 30–50 cm; not necessarily the whole production width
Cameras	Two: complementary views of one surface, or one per surface; interpretation and geometry to confirm
Processing time	Less than 60 seconds from capture to evaluation; precise acceptance definition to agree
Interface	Continuously show the latest captures, highlight suspected defects and raise an alarm
Storage	Persist and catalogue defect evidence and human review
Description	Small image-capable language model desirable, optional
Software	Open-source preferred; dependencies and model licences to evaluate when selected

Current architecture proposal

Two cameras + controlled illumination + trigger
                   |
          Capture and preprocessing
                   |
      Vision / anomaly detection worker
                   |
      SQLite metadata + image filesystem
                   |
        FastAPI + small browser interface
          |                       |
   Immediate alarm         Human review/catalogue
          |
   Optional asynchronous VLM description
   (updates the stored result and interface later)

Detection results, persistence and alarms must not wait for language generation. A VLM can interpret crops; a text-only LLM can only describe supplied features. Predictions remain suggestions until reviewed. The working candidate is an Orin Nano Super 8 GB, subject to image-quality, memory and throughput experiments. An existing computer can support the first acquisition experiments.

This is an experimental inspection assistant. Machine-stop control and production acceptance/rejection integration are outside the current scope.

Documentation

• Architecture: capture, illumination, inference, interface and failure handling.
• Hardware options: historical budgets and unresolved procurement choices.
• Data model: SQLite entities, image storage and review history.
• Roadmap: experiments, acceptance gates and open decisions.
• Discussion provenance: source and treatment of earlier claims.

Software home

src/machine_vision_poc/ reserves the Python package. Its README describes intended boundaries. No dependencies, runtime commands or API endpoints are claimed to work yet. Introduce packaging and pinned dependencies with the first executable vertical slice, starting with recorded images before camera integration.

Keep runtime images, databases, model weights, secrets and generated outputs out of Git. The future application should accept an explicit data root, preferably on SSD and outside the checkout. Git tracks code, configuration examples and documentation; it is not the defect database.

This repository is local. Remote hosting and a project licence remain to be selected by the owner.