Artificial intelligence (AI) and Deep Learning technologies are revolutionizing almost every single industry. And this includes factory automation or production lines that uses machine vision.

Deep learning is helping to automate manufacturing and production in ways that were not possible before. The possibilities are endless as deep learning is one of the many tools that operators can help their companies improve product quality and throughput.

Traditional rule-based machine vision performs reliably with consistent and well manufactured parts and excels in high precision applications. Those include guidance, identification, gauging, and inspection, all of which can be executed at extremely fast speeds and with high accuracy.

This kind of machine vision is great when the variables are well understood.

* Is a part present or absent?
* Is the component the right shape and size?
* Where does the robot need to place the part?

These jobs are easy to deploy on the assembly line in a controlled environment.

But what happens when things aren’t so clear cut?

Take for example in a tin can manufacturing line.
How do you design a program to check for tears or scratches? No two defects are going to look the same.

In theory, you could accomplish this with a rules based algorithm, but it would take a lot of effort to account for all the unexpected variables, and that’s just for one kind of tin can.

Here’s where deep learning comes to the rescue.