The term “deep learning” was all the hype in the machine vision and industrial automation space some years ago. Now, it’s clear that it’s not suitable for all applications. But it does serve in a valuable, complementary role alongside traditional machine vision and automated inspection technologies.
It’s important to understand deep learning before trying to apply it. Unlike rule-based machine vision algorithms, it involves software that learns by analyzing labeled and categorized data human subject matter experts provide.
Deep learning isn’t suitable for precision measurements, location or parameterization, but it excels in subjective inspection decisions, especially for highly variable parts or complex scenes. It simplifies tasks like identifying flaws in textiles or inspecting diverse welds on an assembly line — showcasing its versatility across various industries.
When deep learning is carefully implemented into a new or existing automated inspection system, it proves highly valuable on the manufacturing floor. It’s a practical solution with tangible benefits for manufacturers. It may not be universally applicable, but it consistently delivers real-world advantages where heightened inspection accuracy is critical.
In a recent A3 blog, TECH B2B Marketing’s Jimmy Carroll discusses why deep learning is an important tool for manufacturers prioritizing continuous improvement in inspection processes and how it fits into the automated inspection toolbox.
Read Carroll’s full article on the A3 website to learn more.
“Deep learning consistently delivers real - world advantages where heightened inspection accuracy is critical.”
