Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/28214
Autoria: | Fogaça, J. Brandão, T. Ferreira, J. |
Data: | 2023 |
Título próprio: | Deep learning-based graffiti detection: A study using Images from the streets of Lisbon |
Título da revista: | Applied Sciences |
Volume: | 13 |
Número: | 4 |
Referência bibliográfica: | Fogaça, J., Brandão, T., & Ferreira, J. (2023). Deep learning-based graffiti detection: A study using Images from the streets of Lisbon. Applied Sciences, 13(4), 2249. http://dx.doi.org/10.3390/app13042249 |
ISSN: | 2076-3417 |
DOI (Digital Object Identifier): | 10.3390/app13042249 |
Palavras-chave: | Graffiti Street art Classification Detection Computer vision |
Resumo: | This research work comes from a real problem from Lisbon City Council that was interested in developing a system that automatically detects in real-time illegal graffiti present throughout the city of Lisbon by using cars equipped with cameras. This system would allow a more efficient and faster identification and clean-up of the illegal graffiti constantly being produced, with a georeferenced position. We contribute also a city graffiti database to share among the scientific community. Images were provided and collected from different sources that included illegal graffiti, images with graffiti considered street art, and images without graffiti. A pipeline was then developed that, first, classifies the image with one of the following labels: illegal graffiti, street art, or no graffiti. Then, if it is illegal graffiti, another model was trained to detect the coordinates of graffiti on an image. Pre-processing, data augmentation, and transfer learning techniques were used to train the models. Regarding the classification model, an overall accuracy of 81.4% and F1-scores of 86%, 81%, and 66% were obtained for the classes of street art, illegal graffiti, and image without graffiti, respectively. As for the graffiti detection model, an Intersection over Union (IoU) of 70.3% was obtained for the test set. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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article_95006.pdf | 12,6 MB | Adobe PDF | Ver/Abrir |
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