PPE Compliance Detection AI

This technology can detect non-compliance in real-time and generate alerts, helping to reduce workplace hazards.

Key Features of PPE Compliance Detection AI

1. Real-Time Monitoring:
   • Detects whether workers are wearing required PPE (helmets, gloves, reflective vests, goggles, etc.) through live video feeds.
   • Sends immediate alerts if a violation is detected.
2. Multi-Class PPE Detection:
   • Recognizes multiple types of PPE simultaneously (e.g., helmet + vest + gloves).
   • Differentiates between various colors, shapes, and styles of equipment.
3. Behavior Analysis:
   • Identifies unsafe actions (e.g., workers entering restricted zones without PPE).
   • Tracks proper usage of safety gear (e.g., wearing a helmet properly secured).
4. Customizability:
   • Configurable to specific safety requirements of different industries or facilities.
   • Integrates with existing CCTV systems or wearable devices.
5. Data Reporting:
   • Logs compliance rates, violations, and trends over time.
   • Generates reports for safety audits and regulatory compliance.

How It Works

1. Data Collection:

• Video feeds from CCTV cameras are processed by the AI system.
• Edge devices or cloud servers analyze the footage.

2. Object Detection:

• The AI model (e.g., MOJO V5, Faster R-CNN) detects objects in the video, such as helmets, vests, or goggles.
• It identifies workers and their PPE based on visual characteristics.

3. Classification:

• PPE items are classified and matched against predefined safety requirements.
• Example: The system verifies if all workers in a zone are wearing helmets and vests.

4. Alert Generation:

• If non-compliance is detected (e.g., a missing helmet), the system triggers alerts via:
  • On-site alarms (audio or visual signals).
  • Notifications to supervisors (via SMS, email, or mobile apps).

5. Logging and Reporting:

• Stores data on violations for later analysis.
• Provides dashboards showing compliance statistics.

Technologies Used

1. Computer Vision Models:
   • YOLOv5/YOLOv8: Highly efficient for detecting and classifying PPE in real-time.
   • OpenPose: For identifying human posture and ensuring PPE is worn correctly.
   • DeepLab: For semantic segmentation to detect PPE on individuals.
2. Edge AI Devices:
   • Nvidia Jetson Nano/AGX, Google Coral, or Intel Movidius.
   • Enables low-latency, on-site processing.
3. Cloud Platforms (Optional):
   • AWS Rekognition, Google AI, or Azure Computer Vision for advanced analysis.
4. Programming Frameworks:
   • TensorFlow, PyTorch, or OpenCV.

Applications

1. Construction Sites:
   • Detecting helmets, vests, and safety harness compliance.
2. Manufacturing Plants:
   • Monitoring gloves, goggles, and ear protection usage.
3. Oil & Gas Facilities:
   • Ensuring fire-retardant clothing and breathing apparatus compliance.
4. Healthcare:
   • Verifying masks, gloves, and gown compliance in hospitals or laboratories.

Benefits

1. Improved Safety:
   • Reduces workplace accidents by ensuring adherence to PPE regulations.
2. Cost Efficiency:
   • Minimizes manual inspections and saves resources.
3. Regulatory Compliance:
   • Assists in meeting OSHA or similar safety standards.
4. Scalability:
   • Easily integrates into existing infrastructure and scales to cover large facilities.

Implementation Steps

1. Assessment:
   • Identify PPE requirements and coverage areas in the facility.
2. System Setup:
   • Install or integrate cameras and edge devices.
3. AI Model Training:
   • Train models using sample data specific to your workplace (if off-the-shelf models are not sufficient).
4. Testing and Calibration:
   • Test the system under real-world conditions and adjust thresholds for detection accuracy.
5. Deployment:
   • Activate the system and integrate with alert and reporting platforms.