Deep Learning and Machine Vision for Next-Generation Inspection

Posted: 2019-11-20

Combining machine vision and deep learning will give companies a powerful mean on both operational and ROI axles. So, catching the differences between traditional machine vision and deep learning, and understanding how these technologies complement each other – rather than compete or replace – are essential to maximizing investments. Over the last decade, technology changes and improvement have been so much various: device mobility… big data… artificial intelligence (AI)… internet-of-things… robotics… blockchain… 3D printing… machine vision… In all these domains, novel things came out of R&D-labs to improve our daily lives.

Engineers like to adopt and adapt technologies to their tough environment and constraints. Strategically planning for the adoption and leveraging of some or all these technologies will be crucial in the manufacturing industry.

Let’s focus here on AI, and specifically deep learning-based image analysis or example-based machine vision. Combined with traditional rule-based machine vision, it can help robotic assemblers identify the correct parts, help detect if a part was present or missing or assembled improperly on the product, and more quickly determine if those were problems. And this can be done with high precision.

What Is Deep Learning

Without getting too deep in details, let’s talk about GPU hardware. GPUs (graphics processing units) gather thousands of relatively simple processing-cores on a single chip. Their architecture looks like neural networks. They allow to deploy biology-inspired and multi-layered "deep" neural networks which mimic the human brain.

Read the full article at metrology.news.