The need

Water safety can be difficult to maintain across the vast distribution of a municipal water system. Contamination by bacteria or dangerous particles is often difficult to detect before health issues occur.

The idea

AI detects water contamination issues, using trained models to recognize harmful particles and bacteria. Distributing devices that monitor water for problems will help cities detect contamination as quickly as possible.

The solution

Clean Water AI trains a neural network model, then deploys it to edge devices that classify and detect harmful bacteria and particles. Cities can install IoT devices across water sources to monitor quality in real time.

Technical details for Clean Water AI

Clean Water AI trains the convolutional neural network model on the cloud, then deploys it to edge devices. We used Caffe, a deep learning framework, which allows a higher frame rate when running with Intel Movidius Neural Computing Stick.

An IoT device can then classify and detect dangerous bacteria and harmful particles. The system can run continuously in real time. The cities can install IoT devices across different water sources to monitor water quality as well as contamination in real time.

Currently, Clean Water AI has been built as a proof of concept using a microscope and Up2 board. The entire prototype costs less than $500, and they’re plans to scale up production to help reduce unit costs.

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