Void detection on Edge devices with Live Video Analytics using own images and video
Use a Live Video Analytics module on IoT Edge and deploy a Custom Vision machine learning solution to an IoT Edge device. The solution will identify void spaces in shelves. Check that the solution is successfully deployed and test your solution from a web application.
Learning objectives
In this module, you will:
- Use Live Video Analytics to build video analytics solution with Custom Vision
- Deploy a set of modules to an IoT Edge virtual machine using the installer
- Set up an application that uses the virtual device for rapid inference at the edge
- Deploy a solution that will enable you to watch images with defects through a web application
"Produced in partnership with the University of Oxford – Ajit Jaokar, Artificial Intelligence: Cloud and Edge Implementations course."
Prerequisites
- An Azure subscription
- Ability to use Azure Cloud Shell
- Basic knowledge of Azure IoT Edge
- Basic knowledge of Custom Vision
- Basic knowledge of Live Video Analytics