Personal Protective Equipment Detection | nvidia | AWS |

Overview

The intent of this project is to build an accurate Computer Vision Intelligence model that can detect PPE violations (Hard-hat, reflective Vests, Goggles) at construction sites and other industries to improve workplace safety and compliance with regulations

Solution Overview

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Workplace injuries are a big source of avoidable personal loss and financial loss

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Safety protocols like PPE requirements are routinely violated and traditional measures like safety training, manual monitoring, and safety bill-boards aren't very effective

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Machine Learning models on TensorFlow or Yolo can be trained using annotated data using 'Transfer Learning'.

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These models can be optimized using Hardware accelerators like Intel OpenViNo or Nvidia TensorRT to be able to run in low cost, low power SBCs (Single Board Computers)

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These models can be hosted in the Cloud (AWS, Azure, or Google) to run on video stream from IP cameras to scale easily across any number of regular CCTV cameras.

Business Value

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An automated, real-time Computer Vision Intelligence-based monitoring system that can detect and notify violations to improve compliance.

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The RoI on these automated systems is very high since they use existing CCTV systems and don't require expensive hardware or upfront capital.

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The value of lives saved, and injuries prevented through this automated PPE monitoring is beyond financial measurements.

Technology

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