  Automatically scene understanding using machine learning algorithms has been
widely applied to different industries to reduce the cost of manual labor.
Nowadays, insurance companies launch express vehicle insurance claim and
settlement by allowing customers uploading pictures taken by mobile devices.
This kind of insurance claim is treated as small claim and can be processed
either manually or automatically in a quick fashion. However, due to the
increasing amount of claims every day, system or people are likely to be fooled
by repeated claims for identical case leading to big lost to insurance
companies.Thus, an anti-fraud checking before processing the claim is
necessary. We create the first data set of car damage images collected from
internet and local parking lots. In addition, we proposed an approach to
generate robust deep features by locating the damages accurately and
efficiently in the images. The state-of-the-art real-time object detector YOLO
\cite{redmon2016you}is modified to train and discover damage region as an
important part of the pipeline. Both local and global deep features are
extracted using VGG model\cite{Simonyan14c}, which are fused later for more
robust system performance. Experiments show our approach is effective in
preventing fraud claims as well as meet the requirement to speed up the
insurance claim prepossessing.
