‘m working on a project where I need to detect differences between two printed circuit boards (PCBs): a golden sample (reference) and an actual sample. I want to leverage neural networks for this task but need guidance on the specific technologies and approaches to use.

Here are the details of my project:

I have images or scans of the golden sample and the actual sample PCBs.
The goal is to automatically identify and highlight differences between these two PCBs.

Specifically, I’m looking for advice on:

What type of neural network architecture would be suitable for this image comparison task?

How should I structure the dataset for training the neural network? Should I use pairs of images (golden sample, actual sample) with labeled differences?

What preprocessing steps are recommended for preparing PCB images before feeding them into the neural network?
Any other considerations or best practices for implementing such a system efficiently?

I’m relatively new to neural networks and image processing, so any insights, tutorials, or code examples would be greatly appreciated. Thank you!