Edge AI

Leak Detection and Remote Site Monitoring with AI Models on Edge Devices

Industrial companies can detect and monitor leaks accurately using Computer Vision from Chooch AI by deploying AI onto an edge device. These systems are useful in detecting potential environmental hazards, safety threats, and remote assets that require maintenance. Leak detection provides industrial companies with the ability to monitor key infrastructure in remote areas safely, accurately, and cost-effectively.

Chooch AI’s Leak detection models can catch the spillage or leakage of hazardous materials fast, preventing contamination and environmental pollution. If not monitored properly, hazardous leaks can cause pollution, loss of key assets, and fines from regulatory authorities. Deploying leak detection AI saves companies the time, cost, and risks associated with sending human inspectors to remote sites. Chooch AI models can detect leaks using computer vision through the following methods:

  • Smart cameras installed in remote locations and connected to the Internet of Things (IoT).
  • Drones that collect visual data in the air.
  • Satellite images.
  • PPE detection
  • Object recognition
  • Image recognition

Once the drone captures the image, Chooch AI:

  • Detects leaks and spillages.
  • Counts every drop.
  • Counts how many times a leak/spill happens in a 24-hour period.
  • Distinguishes between hazardous and non-hazardous spills.
  • Sends alerts, reports, and metrics back to decision-makers.

Companies in the utility, energy, construction, and other sectors that have remote assets requiring constant monitoring and maintenance can deploy leak detection and remote site monitoring.

Chooch AI’s leak detection AI models ready for deployment. After successful deployment of an AI model onto edge devices, the remote devices can send alerts when a leak is detected.

Learn more about how AI models are used for infrastructure inspection by reviewing one of our computer vision case studies. Contact Chooch to discuss your leak detection and remote site monitoring project.

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