## 2 Literature Review
### Related Work  
Regarding the detection of acts of vandalism, there are some systems that, instead of only detecting graffiti, also detect the act of graffiti. An example is the work published in [8], which implemented a system that aims to identify stationary visible changes based on the detection of modifications concerning a reference background that is stationary in space and time. However, since other objects, such as people standing still and parked vehicles, can also display stationary patterns in space and time, this system is prone to false positives. Both [10] and [11] try to make the detection more effective by, in addition to analyzing visible changes due to variations of the appearance, also analyzing visible changes due to variations of the 3D geometry of the scene, i.e., the information relative to the depth. This way, this application can improve the results of [8], since new objects in the scene will change brightness and depth; thus, the algorithm will be less prone to false positives. The work in [10] was subject to the TOF (Time-Of-Flight) camera's limitations, since the resolution of the camera used in the experiments was 64 \(\times\) 64 pixels for both intensity and depth images, which did not allow a distance of more than 1.5/2 m between the camera and the wall under test. However, the experimental results of [11] allowed us to verify the robustness of the method used in different situations, such as crowded scenes, abandoned objects, static intrusions, and illumination changes. [PERSON] et al. [13] proposed a system based on an autonomous Unmanned Aerial Vehicle (UAV) that can detect graffitied walls and cover them with spray paint if necessary. This was designed to clean hard-to-reach public places such as bridges and highways. The video stream of the UAV is sent to a machine-learning server containing a trained model developed from scratch for detecting graffiti images. This neural network model was built using the machine learning library TensorFlow. However, the article does not mention any results obtained, thus making it impossible to compare with other algorithms regarding their performance. Similarly, [PERSON] et al. [9] also proposed a semi-autonomous UAV graffiti detection and removal system, but this time based on the SSD MobileNetV2 transfer learning model pre-trained on the COCO dataset from the tensor flow API (Application Programming Interface). However, in this case, two different models were developed, one for graffiti detection on traffic signs and another for graffiti detection on walls. The model for detecting graffiti on walls also recognizes some graffiti styles. The graffiti are classified into Throw up Graffiti, Wildstyle Graffiti, Cartoon Graffiti, Throw up Alphabet, Wildstyle Alphabet, or Cartoon Eye. The tests performed on both models showed an accuracy of up to 99%. However, the authors also state that the system needs to be further tested in more complex environments. In [14], the subject is also the detection of illegal graffiti. The paper proposes the creation of a graffiti map based on the amount of graffiti. Its purpose is to tackle vandalism in places with high concentrations of graffiti and discourage future acts. The model was trained using 632 images acquired in Sao Paulo City, using a ResNet101 backbone model pre-trained on the Coco dataset. The results from this transfer learning method showed an average precision of about 0.57. Studies focused on detecting other types of graffiti, such as murals or street art, can provide information and methods that help in the creation of maps with the exact locations of the artworks for their divulgation to the interested community, as in the study conducted by [PERSON] et al. [7]. This study uses the VGG16 convolutional neural network (CNN) model pre-trained on the ImageNet dataset, with three fully connected layers and a dropout rate of 0.5. Using the binary cross-entropy loss function and the AdaGrad optimization algorithm, an overall accuracy of 93% was achieved.