Multi-modal automotive paint authentication and repainting detection system

Abstract

There is provided a method of automatically detecting paint anomalies on a vehicle, comprising: operating illumination elements configured for generating illuminations of different types at first different frequency bands and/or at first different polarization angles, positioned for illuminating the vehicle, operating image sensors for capturing the images, wherein the image sensors are of different modalities configured for sensing at second different frequency bands and/or at second different polarization angles configured for capturing images of a vehicle, extracting features from the images, generating a multi-dimensional dataset by aggregating the features, feeding the multi-dimensional dataset into a machine learning model, obtaining from the machine learning model, an indication of a region of the vehicle with a paint anomaly, generating an enhanced image depicting the region of the vehicle with the paint anomaly overlaid with a visual indication of the region with the paint anomaly, and presenting the enhanced image on a display.