Reinforement Structure Analysis

I’m working on an AI/ML solution to automatically count the number of horizontal iron bars in rebar cage images taken at construction sites.

The challenge is that a single image contains:

1. Front-face horizontal bars (the ones that should be counted),
2. Rear bars visible through the cage,
3. Interior bars and stirrups,
4. Heavy occlusion and overlapping steel members.

For example, in the attached image, a human inspector would count only the front-facing horizontal iron bar levels.

My questions are:

1. What would be the most robust approach to distinguish front-face bars from interior/rear bars in a single RGB image (or multiple if required)?
2. Has anyone solved a similar problem involving dense repetitive construction structures and occlusion?
3. Would monocular depth estimation (e.g., Depth Anything, MiDaS) be sufficient?
4. Are there classical CV techniques that could outperform deep learning for this specific task?
5. If you were building a production-grade solution, how would you structure the pipeline?

Any suggestions, papers, datasets, or practical experiences would be greatly appreciated. It is not necessary that I have to stick to CV classical approach only.

Attached image for reference (example : one green mark means one horizontal bar).