Detecting the regions where a car has been damaged and the extent to which it has been damaged is a very interesting problem. It has potential applications in automatic auto insurance claims. Currently I'm trying to tackle this problem and am looking for papers/ideas for the same.
I am currently modeling the problem as a segmentation task which can classify each pixel into three classes - background, car, damaged car.
More details: I don't have a lot of data to just train a deep network on. I managed to gather around 700 damaged car images and there are multiple datasets for undamaged cars link. I've tried to generate synthetic data in Unreal using this project, but I'm afraid learning on synthetic data won't generalize to real world images.
Additionally, I found multiple startups working on the same idea but I couldn't find any info on how they're doing it link link. I also found a thesis which aims to do the same link. But it's quite old, and doesn't exactly tackle the actual problem.
I am looking for more ideas on how to formulate the problem and any previous work (like weakly supervised segmentation). Please let me know if you know of any. Thanks!
