| Summary: | Graffiti is common in many communities and even affects our historical and heritage structures. This leads to a decrease in the revenue associated with commercial activities or services (e.g., shops, restaurants, residences), and potentially reduces tourism in a region. Visual data, in the form of photographs, is becoming an efficient mechanism to record information. Photographs can be quickly captured, and are already frequently posted online by ordinary citizens (e.g., tourists, residents, visitors). Exploiting image data through automation and computer vision provides a new opportunity to simplify the current manual graffiti-monitoring processes, enabling automated detection, localization, and quantification of such markings. In this study, we developed a vision-based graffiti-detection technique using a convolutional neural network. Images collected from historical structures of interest within a community can be utilized to automatically inspect for graffiti markings. In the case in which citizens collect and contribute data, there is a high degree of duplication and repetition, and potentially a lack of GPS information. These hinder the direct use of the images for automating the process. To address these challenges, we built high-resolution, single-view façade images (orthophotos) before applying our robust graffiti detector. The robust graffiti detector was built using a database with 1022 images of damaged or contaminated structures gathered during a recent European Union project, entitled “Safeguarding Cultural Heritage through Technical and Organisational Resources Management” (STORM). A total of 818 images were used for training (10% of the training set was randomly chosen for the validation set), achieving 88% accuracy among the remaining 204 samples for testing. Using the trained detector, the technique developed was demonstrated using data collected from the Church of Agios Nikolaos (Leontariou), Kantza, Greece.
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