## 5 Conclusions  
This research work proposed a machine learning-based prototype to automatically detect, classify, and annotate graffiti from the input images. It can support the process, currently done manually by the Lisbon City Council's staff, of identifying walls that require painted for removing illegal graffiti. Furthermore, it also identifies graffiti that may be considered as street art and, consequently, a potential cultural asset to the city.  
Answering the first research question elaborated, \"can a deep learning model successfully discriminate the differences between street art and illegal graffiti?\", the results showed that the proposed deep learning-based architecture successfully discriminates the differences between street art and illegal graffiti. Among the 435 images in the test set, an accuracy of 89% has been achieved. Furthermore, we observed that most of the misclassified images were illegal graffiti whose characteristics were quite similar to other street art images registered on the Lisbon Urban Art Gallery website. In other words, the image databases used for model training and testing contain images that a human himself may have difficulties in distinguishing between the street art and the illegal graffiti cases. Regarding the second research question, \"how accurate is the automatic identification and location of illegal graffiti on images acquired in the streets of Lisbon under loose controlled conditions?\", the IoU metric of the illegal graffiti object detector achieved an average value of about 70%, using test images containing a wide diversity of graffiti samples and locations.  
As for the limitations of our study, all tested image classification models showed poor performance when dealing with images without the presence of graffiti. The best configuration achieved an F1-score of 66%. This behavior is most likely due to the relatively small number of images without the presence of graffiti used during the training process and the great variety among them. For the illegal graffiti detection model, it was verified that its performance was negatively affected by spurious detections due to objects whose patterns somewhat resemble illegal graffiti tags, such as words on billboards  
Figure 22: Pipeline implemented for reaching the most suitable models.  
and posters. In addition, lower-quality images on the dataset were prone to flaws in the detection of graffiti.  
As for further research, a different approach could be followed by using an integrated solution for a street art and illegal graffiti object detection model (instead of using a separate image classification model for this task). Regardless of the approach followed, it would also be important to increase the number of images in the dataset, especially of images without the presence of graffiti. A larger image dataset is expected to improve the training of the models and, subsequently, to achieve better achieve results. Considering the achieved results, it is worth to invest in a more complete system that will the use video cameras mounted in the City Council vehicles already navigating in the city, as well as georeferencing data, to produce automatic annotations for registering and locating the walls subject to graffiti removal and to identify potential street art graffiti.  
[PERSON] performed all technical work related to their master thesis, [PERSON] and [PERSON] were supervisors and helped in the writing process. All authors have read and agreed to the published version of the manuscript.  
Part of this work was supported by Fundacao para a Ciencia e a Tecnologia (FCT), Portugal, under the Information Sciences, Technologies, and Architecture Research Center (ISTAR) projects UIDB/04466/2020 and UIDP/04466/2020. Also part of support from Fish2 Fork E EAGrants PT-Innovation 0069.  
Not applicable.