AI-Driven HSE Non-Compliance Detection for Hazard Prevention
ThirdEye Data developed and deployed an AI-powered computer vision solution to detect real-time Health, Safety, and Environment (HSE) non-compliance across industrial worksites. By analyzing live CCTV footage, the system identifies PPE violations, unsafe behavior, and hazardous conditions, triggering real-time alerts to safety officers. A powerful dashboard provides analytics for ongoing compliance tracking and safety optimization. The production-grade solution is now operational across multiple high-risk sites, helping organizations enforce workplace safety regulations, reduce accidents, and drive operational efficiency.
THE CUSTOMER
BUSINESS GOALS OR CHALLENGES
Business Goals
- Improve Workplace Safety: Detect and respond to non-compliance incidents in real time to minimize injuries and incidents.
- Empower Safety Officers: Enable immediate decision-making through visual alerts and contextual insights.
- Automate Compliance Monitoring: Reduce manual video surveillance by using AI to monitor safety behaviors continuously.
- Maintain Privacy Compliance: Protect employee identity while capturing only relevant safety infractions.
- Create a Scalable Safety System: Deliver a production-ready platform adaptable across various industrial environments.
Understanding the Challenges:
- High Frequency of Violations: Daily safety lapses like missing PPE or unsafe tool usage went unnoticed without real-time monitoring.
- Manual Monitoring Bottlenecks: Human-led CCTV surveillance was inefficient and prone to oversight.
- Behavioral Risk Detection Complexity: Identifying actions like fatigue, improper movement, or slips required deeper temporal and spatial analysis.
- Worksite Diversity: Different layouts, camera angles, and lighting conditions required robust and adaptive AI models.
- Regulatory & Privacy Constraints: The solution had to comply with regional data privacy and workforce transparency laws.
Prerequisites and Preconditions:
To develop this comprehensive HSE non-compliance detection solution, the following setup was implemented:
- Live Video Stream Integration: Ingested real-time feeds from on-premise IP cameras via RTSP/H.264 protocols.
- Labeled HSE Training Dataset: Curated images and video sequences highlighting compliant and non-compliant scenarios.
- Advanced Vision Model Training: Developed custom YOLOv5, CNN, and LSTM models to detect violations and unsafe behaviors.
- Secure & Compliant Infrastructure: Implemented data protection with GDPR-compliant anonymization and robust access control.
- Operational Dashboard Design: Built a web-based dashboard with real-time alerts, historical logs, and compliance heatmaps.
THE SOLUTION
ThirdEye Data delivered an end-to-end, production-ready AI solution for real-time HSE compliance monitoring using advanced computer vision and deep learning techniques.
Solution Highlights
- PPE Violation Detection: Used YOLOv5 to identify missing helmets, gloves, reflective jackets, and other mandatory gear.
- Unsafe Behavior Monitoring: Applied LSTM and Optical Flow models to detect behaviors such as improper bending, fatigue, and tool misuse.
- Hazard Zone Analysis: Recognized blocked emergency exits, spills, and unauthorized access to restricted zones.
- Instant Violation Alerts: Triggered real-time alerts through SMS, email, and dashboard pop-ups for rapid intervention.
- Interactive Compliance Dashboard: Enabled HSE managers to review incidents, view trends, and download reports across locations.
- Privacy Preservation Mechanism: Implemented face-blurring and identity masking while retaining key violation data.
Technologies Used
- Convolutional Neural Networks (CNNs):
For detecting PPE and identifying whether required safety gear is worn by workers.
- YOLO (You Only Look Once):
For fast, multi-object real-time detection to monitor crowded zones, unsafe postures, and movement patterns.
- Recurrent Neural Networks (RNNs) with LSTM:
To analyze sequences of behavior over time and detect fatigue or hazardous motions.
- Optical Flow:
To track motion paths and evaluate unsafe proximity to machines or restricted zones.
- Hough Transform:
To detect environmental anomalies such as circular oil spills or gas leakage patterns.
- FaceNet:
For facial recognition and verifying mask compliance, supporting pandemic control measures.
VALUE CREATED
The deployed AI system has significantly improved site-wide safety compliance and operational transparency:
- Over 90% Detection Accuracy: High precision in recognizing safety gear violations and hazardous behaviors.
- 70% Reduction in Manual Monitoring: Automated surveillance enabled faster, more focused human response.
- Real-Time Risk Mitigation: Enabled proactive responses to unsafe acts before incidents occurred.
- Improved Audit Readiness: Made compliance tracking and reporting more structured and accessible.
- Scalable Across Sites: Successfully deployed in diverse industrial environments with varying infrastructure.
- Strengthened Safety Culture: Reinforced accountability and awareness across the workforce through consistent monitoring.
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