Graffiti and Covered Material Detections in Urban Images using Deep Learning

Graffiti are omnipresent in many cities around the world. Depending on the type of graffiti, these can degrade the aesthetic appearance of urban environments (the vandalistic ones) or even help to promote tourism to a location (the artistic ones). The correct detection of different graffiti types and corresponding covered materials using urban images, will allow government entities to appropriately handling these environments. This paper uses a Deep Learning approach to accurately detect graffiti in complex images and classify them as vandalistic, artistic or wildstyle. Additionally, some common materials that are under the vandalistic graffiti are also classified. Two YOLO-based solutions are proposed and compared using a created dataset of urban graffiti images. This work also provides an experimental analysis on how some types of image distortions (in particular, impulse noise, image blur, random occlusions and image darkening, respectively) impact on the graffiti detection performance task.