Computer vision is a field of study focused on the problem of helping computers to see. At an abstract level, the goal of computer vision problems is to use the observed image data to infer something about the world.
It is a multidisciplinary field that could broadly be called a subfield of artificial intelligence and machine learning, which may involve the use of specialized methods and make use of general learning algorithms like image classification and detection.
Quality assurance is one of the most important things for manufacturers because affects the reputation of a company.A typical production line, components pass from one station to another, and at the end, an inspector steps in to look for problems.
Computer Vision makes it possible to early detect defects on products in a manufacturing context. Executing AI workloads at the Edge, devices spend less time communicating with cloud services and operate reliably even in extended offline periods.
Azure Computer Vision and Azure IoT Edge perfectly fit a defect detection scenario building an intelligent edge.
Computer vision reduces human effort enabling early error detection to improve quality control
AI algorithms are used to train the model for image recognition leveraging the cloud resources
Once the AI model is ready it should be deployed to the edge to improve performance and reliability
Azure IoT Edge moves cloud analytics and custom business logic to devices so that the organization can focus on business insights instead of data management. It’s made up of IoT Edge runtime that runs on each IoT Edge device and manages the IoT Edge modules that are containers that run Azure services, third-party services, or your own code. A cloud-based interface enables you to remotely monitor and manage IoT Edge devices.
Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics.
Azure Custom Vision is used to train the AI model through example images that must be provided; once the model is ready it’s deployed to IoT Edge to start processing images and videos for defect detection. Images upload and tagging could be performed manually using the Azure Custom Vision portal. Another option is to use an IoT Module deployed on IoT Edge that is able to automatically recognize and tag images from a multimedia stream and then upload them to Azure Custom Vision service.
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