Using artificial neural networks for graffiti detection

To solve the problem of detecting graffiti (tags), an artificial neural network with the YOLOv5 architecture was used. This architecture is known for its high operating speed and low computing power requirements, enabling the quick processing of video streams from a large number of city cameras. To account for the regional characteristics of graffiti, a dataset consisting of 1927 photographs was collected. This dataset is open and available at https://noonv.github.io/KGD/. The accuracy of graffiti detection on the test sample was 0.82.