iBubble, powered by the 7.5 watt NVIDIA Jetson TX2 supercomputer on a module, serves everyone from diving enthusiasts and oceanographers to underwater maintenance crews, boat owners and defense departments.
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With advances in machine learning technology, such as SqueezeNet,the embedded AI computer can perform convolutional neural network-powered detection in real time. It uses long short-term memory networks to predict acoustic signals and drastically reduce delays in signal processing.
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The detection and classification systems used are trained on data available on Notilo Plus’ cloud platform. As the number of iBubble users increases, the more underwater data will be collected. Over time, the company plans to build specialized underwater AI datasets and train its deep neural networks specifically on them — increasing the accuracy of iBubble further.
https://blogs.nvidia.com/blog/2018/12/06/ibubble-underwater-drone/
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With advances in machine learning technology, such as SqueezeNet,the embedded AI computer can perform convolutional neural network-powered detection in real time. It uses long short-term memory networks to predict acoustic signals and drastically reduce delays in signal processing.
...
https://blogs.nvidia.com/blog/2018/12/06/ibubble-underwater-drone/
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