As demand for smarter and more efficient manufacturing is growing, IoT technologies—including sensors, edge devices, gateways, servers and the cloud—are being used throughout the factory to compute deep learning analytics workloads at the appropriate location. Efficient data-driven manufacturing can help to reduce labor costs, increase quality and maximize profit. The biggest hindrance to achieving these outcomes is the difficulty in extracting data from vendor-locked and proprietary systems for analytics downstream.
In this presentation, Thimmanaik covers Intel’s approach to developing open, flexible and scalable solutions, including:
- Intel’s technologies such as OpenVINO, Movidius Vision Processor Units, Edge Insights Software (EIS) and deep learning algorithms
- How Intel’s offerings come together in the industrial marketplace with partnerships forged to address the constraints of manufacturing infrastructure
- Real-world examples highlighting defect detection in textile printing (where 90% accuracy at 50 fps was achieved) and smartphone screen production (where false negatives were only 0.6%)
See here for a PDF of the slides.
To view the rest of the 2020 Embedded Vision Summit videos, visit the event's video archive.
