My small stint at the world’s largest e-commerce firm was really fascinating and an enriching experience. I worked for a team that made use of the Industrial Vision system and machine learning to help in sorting and stowing of packages, optimizing the warehouse spaces and the labour hours.
It might seem like a very easy task but it’s not. It’s a complex process. Capturing the information, learning from the trends and continuously maintaining these models to ensure a long-term and robust model. Machine vision is the backbone of operations like the one which I witnessed in one of my previous jobs. And it has it’s used in almost every single industry you can think about. Manufacturing, Automotive, food and packaging and many more – you can check more about its application on this website. It’s an interesting thing to know – science is interesting and a li’l knowledge never hurts anyone.
In an age, when we can not live without technology, it’s amazing to know that it’s application expands to a lot of fields than can be imagined by a normal human mind!
Industrial Vision Systems in the 2020s
And machine vision trends are here to stay. Some of the upcoming trends are very fascinating. I was reading about them here. Does 3D imaging and robotics ring a bell? When they said machines will replace humans, they were not wrong!
3D vision systems can recognize each and everything. Dynamic robotic handling has made picking and stowing of packages of different orientation and sizes. This is something I witnessed in my job. This combined with artificial intelligence reduces human interaction, increases productivity and makes life simpler!
Similarly, human collaborative robots are still the realistic future that can be made possible only by machine and industrial vision!
Application: Deep Learning
I would like to touch upon Deep Learning because that is the field of understanding for me. Deep learning teaches robots and machines to do what a human will do naturally. And vision systems have a major role in this. Visions systems recognize images, perceives trends, understand the subtle changes and feed them to the machines to replicate the behaviour.
This is the ability to learn by example. And it’s completely data-dependent. The bigger your sample size is, the better your machine learns. Deep learning can continuously refine their performances with the addition of more data and information.
How does machine vision help?
Machine vision collects information with consistent camera resolution, optics and learning. This accurate vision inspection has application in even the food and packaging industries. For example, an artificial intelligence supported machine vision not only helps in understanding rigged labels in packaging industry but they also have vast information on the types of rigged label, thus, satisfying the end customers. The probability of dealing with damaged packages and labels decreases drastically.
In food industry, the industrial vision systems provide peace of mind in providing end-of-line quality control to finite detail. These vision systems are robust and reliable and save a lot of time allowing immediate payback on quality and yield.
This topic is something that needs deep research to provide a holistic view. I tried to squeeze in a lot of information in layman language. I help it was a helpful piece of information.