# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
## 3 Materials and Methods
### Generate Synthetic Training Dataset  
A total of 69,120 2D red-green-blue (RGB) synthetic images were created from three different RW-UAVs and three different birds in 3D models. This conversion process involved converting the 3D model file into a 2D image file by taking photos at different angles. 3D models were rotated around the X, Y, and Z axes at defined angles and photographed at every angle change as unique. In this way, the required images were created for the experimental dataset. Figure 2 shows the green box studio which was designed and created in the unity environment to photograph the models and record them as synthetic images in the segment. The background color tone chosen for the green box studio was specially selected to facilitate the background extraction process. In the process of producing all synthetic images, RW-UAV and bird model images were photographed from different angles and were uniquely produced. This uniqueness was ensure by rotating and photographing the models at a certain angle step by step on the X, Y, and Z axes by using the algorithm shown in Algorithm 1, which yielded 34,560 images for each class.  
Images in AI training, validation, and test dataset must be different from each other; therefore, all images were encrypted by utilizing the message-digest algorithm 5 (MD5) and compared to ensure there was no duplication. Encrypting and comparison of image files to avoid duplication is a method that used in the literature [36].  
Figure 2: Green box studio designed in the unity game engine environment: (**a**) Virtual camera used to take a picture. (**b**) RW-UAV 3D model. (**c**) Light sources to generate realistic pictures. (**d**) Green-colored background, wall view. (**e**) Example camera view.  
To measure the prediction success of the DL network trained with synthetic images, a different synthetic images dataset needed to be created. The images involved in the network's training and testing processes must be different from each other. This uniqueness is necessary for the reliability of experiments. A simulation environment that occurs from modern and old city designs was created within the unity game engine to test the network's predictability in daily life. This simulation environment was created for two different cities that have lakes, forest, and buildings; in addition, our designs reflected historical and modern style perspectives. These cities were created with models purchased from the unity asset store [37].  
An image of two cities from different angles are shown in Figure 3. Images generated in the green box were divided into three groups: validation, training, and test stages. The background for the test data was green. The green color was extracted from the background of the synthetic test data, and city images were inserted instead. The dataset preparations for the test and training data are shown schematically in Figure 4. The schematic representation related to the creation of a dataset of the RW-UAV images class was applied to the creation process of the bird-images dataset.The size of all synthetically produced images was set to 224 \(\times\) 224 pixels for comparison and evaluation of experiments. The 90 \(\times\) 90 pixels part of these images consists of the object photograph. Therefore, RW-UAV and Bird images are a 90 \(\times\) 90 pixels portion of the 224 \(\times\) 224 pixels image.  
Figure 4: The schematic representation related to the creation of a dataset of the RW-UAV class.  
Figure 3: Modern and historical city views designed in the unity game engine—virtual environments: (**a**) Game engine terrain of all design. (**a1**) Modern city design. (**a2**) Old city design. (**b–g**) City views from different angles.