[tmotwu]

I worked and built out a proof of concept industrial defect detection system recently, with a large focus on modern DNN architectures. We worked with a plant to curate a 30000+ multi-class defect dataset, many with varying lighting and environment conditions. As you said, modifying and parameter tuning NN is not always a hopeful endeavor.

However, you can make significant gains to your models by going back to traditional image filtering/augmentation. Sticking with well researched object detectors/segmentation algorithms and putting our effort on improving the algorithms that cleans up the data takes you far. It's impossible to avoid because images will always be full of reflections, artifacts, strange coloration unless you have the perfect lighting tunnel setup; doable nonetheless.

[Balgair]

Currently doing the image collection for a NN. Created a custom HW rig to speed things up, lighting, turntables, actuators for novel objects, the works. It's really hard and tedious. We're doing liquid detection and even under IR/UV lights, it's still really hard.

We'd love to be able to work with a company for a few days, get the parameters set up right for our case, and then let them take the thousands of images. My company would easily pay $100K+ for such a data set.