(Not Cognex)
[0] scare quotes because I had the completely free choice between two otherwise identical degrees, one of which had a mandatory year in industry and the other did not, because the UK council tax system demands a different rate if you're a student on a course with a mandatory year in industry
[1] also a cheat, I worked for an academic research lab
My favorite machine vision use case is the tomato sorter. This is one I found from YouTube, not affiliated
And here's documentation on machines which sort grapes intended for wine production:
https://www.food.fraunhofer.de/en/beispiele12/Produktschutz/...
I work in heavy industry (Steel Mill) most people that get hired around here have Engineering degrees. Mining, Oil and Gas is quite similar.
We do engage with data science people etc (Usually as consultants) but there can be a lot of friction as oftentimes the conversation gets lost in translation. If you can fluently speak 'engineer' and 'data scientist' you would make yourself a very attractive hire.
You could also look into the lab automation sector, that is taking off in a big way recently for materials and chemistry (round 2 for chemistry i guess). Chemspeed is the big name but there’s a bunch of smaller companies doing cool vision and robotics stuff in this area
Toothbrushes, drink bottles, oil bottles, more labels than you can imagine, pharmaceuticals, credit cards, fast food packaging, retail packaging, wine bottles, liquor bottles, beer cans, injection molded plastics, IBM bottles, insert molded bottles, cosmetics, raw foods, countless barcodes, and many, many more.
- Agriculture and food processing, which cannot be offshored as easily, requires very challenging machine vision solutions. Dirty environment, unpredictable lighting, unpredictable object appearance.
- Proto and small scale high tech manufacturing, pre-offshoring or sensitive IP, requires machine vision solutions that are both sophisticated and quickly adaptable
I don't think this is true. I work for a US company producing industrial equipment based heavily on machine vision. Our products (along with those of our competitors) have changed the entire industry we support, for the better.
Ours is only one specific part of the manufacturing space, but I fully expect the impact to spread to other parts as well.
Being able to identify molds reaching end of life prior to parts failing QA for being out of tolerance is also huge for American manufacturing.
Where it's way less important is when you are spitting out eraser tips or other 'high scale' manufacturing.
In the U.S. the number of people employed in manufacturing is lower than ever but the value of manufacturing has been steady at 12% of GDP since World War 2 ended.
As for PC-based systems, I would be very surprised if deep learning models weren't being used in production somewhere. But in a factory environment you can go a very long way with primitive feature recognition and good control over the scene and lighting, and the customer just cares that whatever you're doing just works and any new method will have to be enough of an improvement to be worth the cost of development time.
They definitely are. ~5 years ago I built a PC-based system that detected grain direction of wooden boards (looking at the end of the board).
Initially I resisted the ML approaches and my first attempt was basically hand-crafted image analysis pipeline- split the image in segments, apply Gabor filter with kernels of various angles and try to fit a curve to results. It kind-of-worked but I wasn't entirely happy with it's performance on the test data.
Even the simple classifier models that could execute on a fanless PC without a GPU outperformed my solution, and after a few more training runs the handcrafted code was replaced by #include <tensorflow.h>.
This year I'll have to extend the system with on-site training mode, where an operator has a pushbutton to label the images and re-train the model.
Honestly though, the suits i worked with, were all very dated and used hand constructed feature filters etc. to detect flaws. Usually, it was easier to adapt the environment (exclude external light etc.) instead of lengthy tuning sessions for the installer.
Usually the industrial cameras were also designed, so that local maintainers could readjust them, which excluded complex programming and happened in simple wizards or excel like programming surfaces. There was no time planned in to "retrain" further once the line was running. And it was cheap and good enough that way.
Thus the "cutting" edge tech seemed to be eternally 20 years behind the cutting edge in other sectors relying on machine vision.
That demo of real-time blob detection and sorting by color filtering was doable in 1998. Earlier than that, even. I've found about 90% of the work in vision applications in industrial packaging is in the product handling and scene setup - focal length, lens selection, exposure time, etc. - all things familiar to a photographer. The last 10% is almost always handled by bog simple algorithms that can be more or less cobbled together from OpenCV's examples and boilerplate, the most complicated usually being OCR.
The value-add of these dedicated industrial vision systems is in integration. Fanuc's iRVision is good at sending spatial data back to the robot controller, but the interface itself is a horrid kludge that specifically requires Internet Explorer and in-person training at their own (admittedly very nice) facilities and promises of litigation if you so much as think about sharing documentation with co-workers.
Recording images during trial runs with their native tooling was impossible, as their under-powered processor couldn't handle saving 640x480 images at 10fps while also running the vision application. So we resorted to recording test runs by feeding the live view OBS, and everyone thought I was some kind of wizard for even considering that.
At least Cognex's In-Sight has the ability to simulate their weird spreadsheet-based vision programs without a camera. With Fanuc you need the whole $30,000+ robot+controller+camera setup and with real-time applications the only way to debug it is to run it in situ.
Now my most recent industrial vision experience is from 2019, so maybe some things have changed. But these are folks that often don't even know what source control is and will run screaming for the hills at the first sign of anything that's not Excel or ladder logic, and balk at the idea of paying an experienced engineer more than $100k all the while wondering why they aren't finding any talent.
I’m hugely enthusiastic hobbyist that would love to chat more about robotics, in particular how a hobbyist could get started with it (a robot arm + camera maybe?). I’d love to buy you virtual coffee, get in touch if you’re up to it!
I did a project with Lego sorting marbles in 2009 doing this in High School. By that point, anyone with $100 and a few hours of spare time could put together a rudimentary sorting system.
Reasons why I think it shows off their best features:
- two regular industrial cameras 1.5m away
- shiny and slippery parts
- vacuum gripper (not magnetic)
- cramped picking environment
- works even when things move around (no scene caching)
- fast (video is not sped up)
https://www.valcomelton.com/industrial-products/inspection-c...
