FlytBase will be releasing its AI platform for drone applications at the Drone World Expo, San Jose, on 3rd October 2017. FlytBase has built the world’s first IoT platform for commercial drones, the “Internet of Drones” (IoD) platform. Continuing on its mission to bring intelligence and connectivity to commercial drones, FlytBase is now extending its cloud and edge compute platforms to incorporate AI and machine learning.
Drones generate vast amounts of data, which is usually in the form of images or video streams. Identification of objects of interest, counting them, or detecting change over time, are some of the tasks that are monotonous and labor intensive. FlytBase AI platform offers a complete solution to automate such tasks. It has been designed and optimised specifically for drone applications. The cloud-based training system leverages the scalability of the cloud to accelerate the training of models, to suit various customer requirements. Based on the use-case, the trained model can be deployed in the cloud (for post-processing of data) or on the edge (for real-time analysis).
FlytBase AI platform is optimised for interpretation of drone data, and it seamlessly integrates with the rest of FlytBase platform to offer connectivity with your business applications.
Be the first one to know more about FlytBase AI Platform, signup to stay tuned. Visit: https://flytbase.com/ai
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You can join Nitin Gupta, CEO FlytBase Inc. in the panel discussion with other leaders from the Industry on The Role of IoT, Software & Platforms in the Commercial Drone Ecosystem
At Drone World Expo October 3, 2017 | Conference Room No. 1, San Jose Convention Center, San Jose, CA
Event Link: http://www.droneworldexpo.com/sessions_detail.asp?id=4425
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Comments
That is a fair point. The workflow can be used for all sorts of tasks. However, your AI engine will only be as good as the training data. For several applications (like the one that has been illustrated in the release -- counting objects), the data collected from drones (top view) is very different from that collected using other sources. Our interest is in fine-tuning the engine (building a repository of models) to make it super-smart for drone applications, in particular.
Cool concept. Why limit to drones though? Finding cracks on a bridge is the same whether the image is obtained from a drone, a remote controlled robot, a crane or a handheld camera.