Abstract
Art graffiti can be considered as a type of urban street art which is actually present in most cities worldwide. Many artists who began as street artists have successfully moved to mainstream art, including art galleries. In consequence, the artistic graffiti produced by these authors became valuable works which are part of the cultural heritage in cities. When understanding the economic value of these public art initiatives within the smart cities context, the preservation of artistic graffiti (mainly, against vandalism) becomes essential. This fact will make it possible for municipal governments and urban planners to implement such graffiti maintenance initiatives in the future. In this context, this paper describes a deep learning-based methodology to accurately detect urban graffiti in complex images. The different graffiti varieties (i.e., 3D, stencil or wildstyle, among others) and the multiple variabilities present in these artistic elements on street scenes (such as partial occlusions or their reduced size) make this object detection problem challenging. Our experimental results using different datasets endorse the effectiveness of this proposal.
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Acknowledgements
We acknowledge to the Spanish Ministry of Science and Innovation, under RETOS Programme, with Grant No.: RTI2018-098019-B-I00; and also to the CYTED Network “Ibero-American Thematic Network on ICT Applications for Smart Cities” (REF-518RT0559).
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Bomfim, T.S., Nunes, É.d.O., Sánchez, Á. (2022). Art Graffiti Detection in Urban Images Using Deep Learning. In: Sappa, A.D. (eds) ICT Applications for Smart Cities. Intelligent Systems Reference Library, vol 224. Springer, Cham. https://doi.org/10.1007/978-3-031-06307-7_1
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