IRIS
AI-powered PPE detection and workplace safety monitoring system — real-time compliance checking, violation alerts, and safety analytics for industrial environments.
Forked from Nedo Vision — our enterprise computer vision framework.
What is IRIS?
IRIS is an AI-powered PPE (Personal Protective Equipment) detection system built on top of our Nedo Vision framework. It uses deep learning models to automatically detect whether workers are wearing required safety equipment — hard hats, safety vests, goggles, and boots — delivering real-time compliance monitoring and instant violation alerts.
The Challenge
Petrochemical plants face critical workplace safety challenges that traditional monitoring methods cannot adequately address.
Manual Safety Inspections
Safety officers manually patrol large plant areas to check PPE compliance — slow, inconsistent, and impossible to cover all zones simultaneously.
Blind Spots & Coverage Gaps
Critical high-risk zones in the petrochemical plant often go unmonitored, leaving workers exposed to hazardous conditions without proper equipment checks.
Delayed Violation Response
PPE violations are only discovered during periodic audits, not in real-time — meaning workers may spend hours in hazardous areas without proper protection.
No Data-Driven Safety Insights
Without systematic tracking, there's no way to identify patterns in safety violations, high-risk time windows, or compliance trends across departments.
Data Engine
Acquire, annotate, version, and manage your training data pipeline end-to-end.
Automatic Data Acquisition
Continuously build and improve training datasets directly from live camera feeds or offline video — no manual frame extraction required.
Live Camera Feed
Automated Training Data Pipeline
Continuously capture frames from RTSP/ONVIF camera streams deployed across the plant. The system intelligently selects high-value frames — filtering out duplicates, low-quality, and redundant poses — to build a diverse training dataset automatically.
Web-Based Annotation Studio
A full-featured annotation workspace built right into the platform — draw bounding boxes, polygons, and polylines with professional-grade shortcuts. No external tools needed.
Automatic Model-Assisted Annotation
The existing model auto-labels newly acquired frames — high-confidence predictions become instant annotations while uncertain detections are flagged for human review.
Dataset Versioning
Every dataset iteration is version-controlled — track changes, compare accuracy across versions, and roll back to any previous state instantly.
Active Learning
Automatically surface the most uncertain and informative frames for human annotation — reduce labeling effort by up to 70% while maximizing model improvement per labeled sample.
Dataset Analytics
Visualize class distribution, label quality, and dataset health at a glance — catch imbalances, missing labels, and duplicates before they hurt model performance.
Training Lab
Augment data, configure hyperparameters, and launch distributed training runs on any infrastructure.
Intelligent Data Augmentation
Automatically apply a rich set of augmentations to multiply your training data — increasing model robustness against real-world variations in lighting, angle, scale, and color.
Direct Training From the Platform
Launch distributed training runs directly from the platform — deploy training agents on cloud, on-premise, or hybrid infrastructure. Configure, monitor, and deploy without ever leaving the interface.
Leverage elastic cloud GPUs for burst training — auto-scale across multiple nodes and pay only for what you use.
Hyperparameter Tuning
Automatically search for the best training configuration — sweep learning rates, batch sizes, augmentation strategies, and more with grid, random, or Bayesian search.
Model Benchmark
Compare multiple model versions side by side — mAP, FPS, latency, size. Find the perfect accuracy-speed tradeoff for each deployment target.
Deployment
Version, compare, and deploy trained models to production with zero-downtime rollouts.
Model Versioning & Deployment
Every trained model is versioned and tracked. Compare performance across versions, promote candidates through environments, and deploy the best model with a single click.
Deploy Anywhere with Runner Agents
Install lightweight runner agents on any platform — Windows, Linux, macOS, or edge devices like NVIDIA Jetson. Each agent connects back to the platform for centralized management, updates, and monitoring.
Zero Data Loss — Even Offline
When network connectivity drops, the runner agent continues inference and queues all results, frames, and metrics locally. Once reconnected, everything syncs back automatically — no data is ever lost.
One Manager, Every Agent
A single centralized dashboard manages all runner agents across cloud, on-premise, and edge deployments — unified model rollout, monitoring, and control from one place.
Deploy on Drones
Mount IRIS on industrial drones for aerial PPE detection across large facilities. Autonomous patrol routes, real-time streaming, and edge inference on-board — covering areas fixed cameras cannot reach.
A/B Testing & Canary Deployment
Gradually roll out new model versions — 5% → 25% → 50% → 100% of agents. Monitor metrics at each stage and auto-rollback if error rate spikes.
Model Optimization
Export and optimize models for any target — TensorRT, ONNX, INT8 quantization. Achieve 3x faster inference with minimal accuracy loss, ready for edge deployment.
Observability
Monitor model performance in real-time, detect data drift, and track inference metrics across deployments.
Real-Time Violation Dashboard
Every PPE violation is captured, categorized by severity, and displayed in a live dashboard — enabling instant response, audit trails, and compliance reporting.
Webhook & MQTT Event Stream
Every detection event is broadcast via webhooks and MQTT — integrate with Slack, SMS, ERPs, custom dashboards, or any system that can receive HTTP or MQTT messages.
{
"event": "no_hard_hat",
"zone": "zone-a",
"confidence": 0.972,
"timestamp": "2026-02-28T14:32:08Z",
"camera_id": "CAM-04",
"frame_url": "https://iris.ai/frames/..."
}Visual Workflow Builder
Build conditional detection pipelines with a flow-based visual editor — wire together triggers, conditions, and actions to create sophisticated rules like "only count workers when a ship is in frame."
Camera Grid
See all camera feeds simultaneously with real-time detection overlays — PPE status, worker count, and violation alerts on every feed at a glance.
Automated Reports
Auto-generated weekly and monthly compliance reports with violation trends, zone heatmaps, and improvement tracking — export as PDF for audits.
Data Drift Detection
Continuously monitor input data distribution against the training baseline — detect camera changes, lighting shifts, and novel scenarios before they degrade model performance.
Impact & Results
Measurable safety improvements at PT Petrokimia Gresik since deploying IRIS.
Fewer Violations
Dramatic reduction in PPE non-compliance incidents across monitored zones.
Detection Speed
Real-time inference on edge devices enabling instant violation detection.
Detection Accuracy
Industry-leading precision trained on Petrokimia Gresik's specific PPE requirements.
Monitoring Coverage
Continuous automated surveillance across all high-risk plant zones.
Technology Stack
Built on Nedo Vision's proven AI framework with edge computing for real-time performance.
AI & Vision
Backend & Infrastructure
Frontend & Dashboard
Ready to Enhance Workplace Safety with AI?
Deploy AI-powered PPE detection across your industrial facilities. Built on our proven Nedo Vision framework, customized for your safety requirements.