{"id":404,"date":"2020-02-04T15:56:43","date_gmt":"2020-02-04T13:56:43","guid":{"rendered":"http:\/\/wixflytt.acconeer.com\/?p=404"},"modified":"2021-07-19T21:25:46","modified_gmt":"2021-07-19T19:25:46","slug":"deep-learning-support-available-in-exploration-tool","status":"publish","type":"post","link":"https:\/\/acconeer.com\/sv\/news\/deep-learning-support-available-in-exploration-tool\/","title":{"rendered":"Deep learning support available in Exploration Tool"},"content":{"rendered":"\n

The high-resolution output from the Acconeer radar sensor is perfect for use with deep learning algorithms. Using the new Deep Learning Interface (DLI) in our Exploration Tool<\/a>, you can record data and train your own Artificial Intelligence (AI) model without any need for programming. This is useful when evaluating and implementing use cases such as gesture detection for control of electronic devices, surface classification for use in robotics and many more.<\/p>\n\n\n\n

The DLI is built on the open source AI tools TensorFlow and Keras, and lets you train and use AI networks with the A111 radar sensor in a simple and convenient way. To make it easier to evaluate and implement radar-based use cases built on AI, we have also released detailed DLI documentation<\/a> explaining how to use it and the steps needed to get started.<\/p>\n\n\n\n

One example of how the deep learning in Exploration Tool can be used has been implemented as a master’s thesis collaboration between Lund University and Acconeer. In her work, Mathematical Statistics student Eda Dagasan trained an AI network to recognize a set of gestures using the radar data from Acconeer\u2019s A111 radar sensor. The resulting model managed to identify gestures with more than 95% accuracy. See the model in action in the video below.<\/p>\n\n\n\n

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