Vision systems in controlled environments like factories that need to be fast and stupidly reliable are often based on "classical" (non-ML) computer vision techniques.
The ML hype train has led to a frustrating amount of throwing the baby out with the bathwater, where people who should know better decide to use ML for more things than is reasonable (something something "end-to-end").
Ideally, ML should be used for a few very specific tasks, and then classical machine vision / geometric analysis / plain old logic should be doing the rest. If you don't do it this way, you eventually end up with the problems described in this paper, where performance is inconsistent and impossible to debug, and nobody can tell you what's going on or why.
This is years ago and not my specialty, but I worked with a product line that manufactured what were essentially highly precise plastic washers. As they came off the line there was a clever bit of mechanical disbursement that slowly dropped them onto a conveyor under which a flashing light sync'd with a digital camera system took pictures.
Because all of the factors are known you could almost do pixel counts and compare to "true" circles (no need for AI models), any washers that didn't meet the critera were pneumatically puffed off of the line from an air hose.
The ML hype train has led to a frustrating amount of throwing the baby out with the bathwater, where people who should know better decide to use ML for more things than is reasonable (something something "end-to-end").
Ideally, ML should be used for a few very specific tasks, and then classical machine vision / geometric analysis / plain old logic should be doing the rest. If you don't do it this way, you eventually end up with the problems described in this paper, where performance is inconsistent and impossible to debug, and nobody can tell you what's going on or why.