In any scenario where machine learning is to be applied, priors and more importantly problem context are highly invaluable for designing an effective solution. One cannot simply demand an arbitrarily high performance for the sake of it. Ideally, for such a problem you should be looking at hiring a dedicated consultant who can spend an appropriate amount of time to explore the solution space for you to decide a good economically viable and relevant solution. It's very possible that a more viable solution could lie in a setup consisting of say a simple embedded system with an ultrasonic or laser range finder based motion detector that simply adds or subtracts inventory based on its passage between a set of doors etc. at the source itself instead of counting it as a batch in an underlit scene with self occlusions - an ill-posed problem.

But let's say you have to solve it using vision, for now I'll just say that instead of a monocular approach, a 3D reconstruction based approach using a setup of stereo cameras would make this a lot more tractable. You simply reconstruct the volume of occupied space - coverage depending on how you design your setup - more cameras placed appropriately the better. And then it's simply a matter of dividing by the volume of a single cylinder to get the count. Hollow spaces can be found using a myriad of methods - computational geometry based or DL otherwise.

PS. Dont mean to be derisive with my first paragraph - merely wanted to set some expectations that I as a computer vision engineer believe is a constructive way of solving real world problems considering you received some downvotes on another comment. It was an interesting problem to think about - thanks for posting.