Monitor and Alert for PPE Compliance

Reduce workplace incidents by autonomously identifying PPE non-compliance with Monitor and Alert

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Monitor and Alert

Summary

With the current fast-paced development ecosystem and increased labor protection laws, monitoring the use of Personnel Protective Equipment’s (PPEs) has developed into an indispensable task for industries operating in hazardous environments.

Computer Vision combined with Machine Learning can automate the process of monitoring PPE compliance. Existing CCTV’s can be integrated with Artificial Intelligence that use the concept of Bounding Boxes to autonomously identify personnel who are not using or improperly using PPEs.

Edge computing systems enable real-time monitoring of PPE compliance with respect to the work environment and can send signals to the Safety and Maintenance department in case of any PPE violations.

The data collected and stored in cloud-based, or edge devices based on organizational needs can be used for audit purposes and to generate reports automatically using Business Intelligence tools. The technology aids in the reduction of down-time and legal expenses stemming from PPE non-compliance.

Data and Factors Considered

  • Computer vision data
  • Real-time object detection data
  • Bag of Features to train AI models
  • PPE requirements data
  • Downtime history data relating to PPE non-compliance
  • Log data from security checks and PPE checks
  • Industry and regulatory standards

Data Processes and Key Technologies

Feature Engineering and Modelling

  • Neural networks to detect PPEs based on workplace requirements.
  • Model trained using a combination of images with and without PPEs.
  • Object detection models with bounding boxes for classification of worker images.
  • Boosting to iteratively improve the model based on identified alerts and false positives.

Key Technologies

  • Computer vision and Edge Computing to rapidly process information and minimize latency.
  • Real-time video analysis combined with state-of-the-art algorithms to monitor PPE compliance.
  • Neural compute engines used to enhance performance of neural network models and rapidly perform calculations.
  • AI integrated with CCTVs to monitor movement and identify absence of PPEs.

Outcomes

  • AI-powered PPE monitoring that can identify non-compliance in real-time.
  • Real-time monitoring helps study the root cause of incidents and take appropriate steps to mitigate them.
  • Reduction in workplace incidents compensation costs.
  • Reduced manual intervention in investigating PPE non-compliance.
  • Automated reporting for regulatory compliance.
  • Provides 24/7 around the clock analytics.
  • Compatible with existing high-resolution cameras and can be scaled up as per industry regulations.