Real-Time Object Detection for Factory 4.0
Edge Impulse FOMO (Faster Objects, More Objects) is a novel machine learning algorithm that brings object detection to highly constrained devices. It lets you count objects, find the location of objects in an image, and track multiple objects in real-time using up to 30x less processing power and memory than MobileNet SSD or YOLOv5. In this exercise, attendees will learn how to collect a high-quality object detection dataset to train and deploy a FOMO model to a microcontroller like the Arduino Portenta H7 + Vision shield.
Some Factory 5.0 example use cases:
- Scan for defective goods during the packaging processes
- Use machine vision to find objects or humans in restricted areas
- Use computer vision for quality control of pests in agriculture
- Deploy AI to control the goods on the shelves of the supermarket
FREE BY APPLICATION - APPLY HERE
Limited to 50 participants:
- Certificate of accomplishment from Edge Impulse
Jenny Plunkett
Jenny Plunkett is a Texas Longhorn and software engineer, working as a Senior Developer Relations Engineer at Edge Impulse. Since graduating from The University of Texas she has been working in the IoT space, from customer engineering and developer support for Arm Mbed to consulting engineering for the Pelion device management platform. Jenny is also the co-author of "AI at the Edge: Solving Real World Problems with Embedded Machine Learning" by O'Reilly.
