# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
###### Abstract
Data collection and data preparation processes are labor-intensive, creating a significant workload that is repeated for each new class when training the ML model [8]. More recently, the success of image classification research has increased with the development of deep learning-based ML models. Classical ML techniques include operations such as feature extraction and selection. Feature extraction and selection layers are manual processes in classical ML, and these layer requirements have been automated in ML models based on deep learning (DL) [9]. The automation of these layers can be considered to be an advantage of DL networks; however, this advantage has the disadvantage that DL networks require more training data [10]. The most-used method in deep learning-based image classification studies is the convolutional neural network (CNN). CNN with deep learning-based networks is a rapidly developing technology, and is becoming a more widespread method for image data-type classification studies [11]. The best-known image classification competition in recent years is the IMAGENET large-scale visual recognition challenge (ILSVRC). The highest-ranked participants used CNN-based methods in ILSVRC [6]. CNN networks consist of two core structures, called the convolution and pooling layers. The convolution layer consists of feature maps, with each connected to each other and consisting of a series of weights, and eventually connects to a function such as a rectified linear unit (RELU). Networks frequently used in image classification studies with CNN are AlexNet, Visual Geometry Group Network (VGGNet), and SqueezeNet architectures. There are studies that have achieved RW-UAV and bird image classification by using deep learning-based architectures [12]. The image data used for training and testing in these studies consist of real photographs that were taken manually. Real photographs of objects belonging to each class must be created to train AI in the general approach to AI model training. The disadvantage of AI studies in which real photo data are used is that real data collection is a difficult and labor-intensive task. In addition, taking photos of real data is difficult depending on the outdoor environment conditions, such as altitude, danger zone, and adverse weather conditions. Considering the importance of the diversity and amount of data in AI training and the difficulty of creating a dataset manually, the following research questions need to be addressed: \"Can the artificial intelligence training-image dataset be synthetically produced as needed?\" and \"Can an artificial intelligence model trained with synthetic data be used to classify a real-image dataset?\". In this study, we have focused on increasing the accuracy of results obtained by trained deep learning-based ML models with a fully synthetic-image dataset and tested them with a real-image dataset. To create a synthetic dataset, a virtual studio was created by using the Unity game engine, and 69,120 synthetic images were created for bird and RW-UAV three-dimensional (3D) models. In total, 34,560 synthetic RW-UAV images and 34,560 synthetic bird images were produced, and the 216 ML models were trained with these synthetic images. Models were tested with 3385 real images and 5000 synthetic images separately. We propose a new approach to improve the accuracy of the DL networks trained with synthetic images and tested with real images. The proposed approach, corner detection, and nearest three-point selection (CDNTS), is a hybrid method of corner detection methods and the nearest point search method for feature extraction that can be used with DL networks. Corner detection-based methods are commonly used in visual tracking [13], image matching and photography [14], robotic path finders [15], etc. This approach is a technique that is generally used to detect corners in images in image-based studies. The \"nearest three-point selection\" part that forms the other half of the hybrid CDNTS layer is a function that searches for the closest points.