I'm new to AI but would still like to try and get a project off the ground. I've read a lot about ML/DL the past few days but I just can't figure out if my problem can be solved with ML/DL. What I'm trying to do looks like a classification job to me but maybe isn't.
I have 100s of images of compacted soil samples, on these images there may be multiple layers visible. I will include a picture below, this sample had a sticker on it, normally they don't. On the image there are 3 layers, separated above and under the sticker. With every image there is data (xml file) available on the size of the layer(s) and the type of soil in that layer, which costs a lot of time to produce, so I want to automate this classification in the future. The data files contains info like:
layer0: type 004 2cm 12cm layer1: type 003 12cm 25cm
If there would be just one layer, the AI could learn what these layers look like and sort them in the right soil class. But I don't know if my problem can be solved with AI as there could be 1, 2, 3 or 4 different layers (classes) on one image and I haven't seen any examples on classification where there can be multiple classes in one image. As AI is quite a steep learning curve I would like to know if my problem is suited for ML/DL before I spend more of my nights reading for something that might not work. I've read numerous websites and a few short books but can't find an answer to my questions.
Can ML/DL solve my multi-class single-image classification problem and which strategy should I read into?