![]() ![]() ![]() ![]() These hinder the direct use of the images for automating the process. 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. Images collected from historical structures of interest within a community can be utilized to automatically inspect for graffiti markings. In this study, we developed a vision-based graffiti-detection technique using a convolutional neural network. 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. Photographs can be quickly captured, and are already frequently posted online by ordinary citizens (e.g., tourists, residents, visitors). Visual data, in the form of photographs, is becoming an efficient mechanism to record information. 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. Graffiti is common in many communities and even affects our historical and heritage structures. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |