Augmented Safety Monitoring using Compliance Video Analytics for a Large Oilfield Services Company

Case Study

Augmented Safety Monitoring using Compliance Video Analytics for a Large Oilfield Services Company

Business Objective

Our client is a leading oilfield services provider based in the US. The client had a manual process of monitoring safety events like checking if employees are wearing hard hats, monitoring unauthorized entry into restricted areas, and checking if pipes are aligned properly.

The client wanted a real-time video analytics platform to automate the site’s monitoring process, have an automated alert generation in place, and an AI-based entry log for restricted areas.

Challenges

  • Camera placed far from the work floor and is susceptible to fog and light reflection
  • Construction sites are cluttered with objects, and the occlusion of large objects is a major concern
  • Need for a fast and efficient model that can process five frames per second on an HD video stream

Solution Methodology 

  • Analyzed client-provided video and other sourced datasets such as Visual Genome, CAVIAR
  • Used YouTube videos and Google images to have sufficient training datasets
  • For pipe alignment, used client-provided videos to train the model. For the hardhat and restricted area entry, used Visual Genome, CAVIAR, and other YouTube videos and Google images
  • Extracted images using OpenCV and manually annotated them using the Labelling tool
  • Trained and tested models for object detection and object segmentation
  • Developed the capability to raise alerts in the form of light, alarm, email, or SMS through a flag in the system
  • Deployed models with client surveillance feed coming in
  • Pre-processed images using deep learning models to remove fog and glare leading to the detection of hard hats, multiple people tracking, and pipe alignment.

Business Impact

  • Identified people, hardhats, and hardhat colors in different scenarios with 100% accuracy for person class and 98% for hardhat class
  • Built a self-diagnostic module (SDM) that gives the working status of the video feed (checks for quality, light source detection)
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