Industrial Safety Vision AI

Motivation

Industrial sites need continuous safety monitoring, but hazardous events (missing PPE, unsafe proximity) are rare in real footage — which starves any model of training data. The project had two halves: a real-time analysis pipeline to watch live video, and a generative pipeline to synthesize realistic edge-case imagery so the system could learn what danger looks like.

Architecture

A Vision-Language Model interprets video frames against safety prompts in real time, while a separate FLUX-based generator produces synthetic training data for rare scenarios. Everything is observable through a Streamlit interface and a structured logging framework.

What I built

• End-to-end Gen-AI for industrial safety — both image generation and real-time video analysis pipelines.
• A customizable image generator on HuggingFace's FLUX model with both GUI and headless (non-GUI) modes.
• A logging framework capturing prompt and configuration data for full traceability and reproducibility.
• Multi-threaded video analysis with Qwen VLM, OpenCV and Streamlit for real-time monitoring.

Results

Challenges

• Latency vs. accuracy: VLM inference is heavy; multi-threading the frame pipeline kept analysis real-time without dropping critical frames.
• Data scarcity: real hazards are rare, so FLUX-generated synthetic scenarios filled the gap with controllable, labeled edge cases.
• Reproducibility: generative output is sensitive to prompts/config — the logging framework made every result traceable.

Learnings

• Synthetic data is a force multiplier when real edge cases are scarce or dangerous to capture.
• VLMs unlock open-vocabulary monitoring — you describe hazards in language instead of training a fixed-class detector.
• For real-time CV, the systems engineering (threading, I/O) matters as much as the model.