# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
###### Abstract  
A large amount of training image data is required for solving image classification problems using deep learning (DL) networks. In this study, we aimed to train DL networks with synthetic images generated by using a game engine and determine the effects of the networks on performance when solving real-image classification problems. The study presents the results of using corner detection and nearest three-point selection (CDNTS) layers to classify bird and rotary-wing unmanned aerial vehicle (RW-UAV) images, provides a comprehensive comparison of two different experimental setups, and emphasizes the significant improvements in the performance in deep learning-based networks due to the inclusion of a CDNTS layer. Experiment 1 corresponds to training the commonly used deep learning-based networks with synthetic data and an image classification test on real data. Experiment 2 corresponds to training the CDNTS layer and commonly used deep learning-based networks with synthetic data and an image classification test on real data. In experiment 1, the best area under the curve (AUC) value for the image classification test accuracy was measured as 72%. In experiment 2, using the CDNTS layer, the AUC value for the image classification test accuracy was measured as 88.9%. A total of 432 different combinations of trainings were investigated in the experimental setups. The experiments were trained with various DL networks using four different optimizers by considering all combinations of batch size, learning rate, and dropout hyperparameters. The test accuracy AUC values for networks in experiment 1 ranged from 55% to 74%, whereas the test accuracy AUC values in experiment 2 networks with a CDNTS layer ranged from 76% to 89.9%. It was observed that the CDNTS layer has considerable effects on the image classification accuracy performance of deep learning-based networks. AUC, F-score, and test accuracy measures were used to validate the success of the networks. image classification; deep learning; CNN; corner detection; rotary-wing UAV; synthetic image generation; corner detection and nearest three-point selection (CDNTS) +
Footnote †: journal: Applied  
been used for detection and identification purposes. The above-mentioned radar, voice, and image-based UAV detection methods utilize antenna, microphone array, and camera hardware, respectively. Machine learning (ML) methods are among the most frequently used methods to classify UAVs with sensor fusion data. Image classification methods with artificial intelligence (AI) are used in UAV classification studies [3]. Image classification methods with AI consist of a series of processes such as data collection, data preparation and labeling, training, and testing [4]. The data type for training ML models in image classification problems is image. Data collection and data preparation are accomplished by taking photos of the relevant class or by collecting and screening from the Internet; later, they are labeled individually by hand [5]. The process of collecting the data, such as images, radar fingerprints, or voice fingerprints, that are required for ML model training is labor-intensive and time-consuming. In the RW-UAV detection process, it is important to identify birds that are likely to be in the sky, as well to avoid any confusion in application. Therefore, a labeled dataset should consist of the image classes of birds and RW-UAVs to develop a successful ML identification model that can classify birds and RW-UAVs. The methods used to create a dataset include ready-made datasets such as IMAGENET [6] and CIFAR10 [7], or photographing objects at different angles for classes without datasets. The collection of data and the arrangement of these collected data according to the ML model is called data collection and preparation, which is a preliminary process for training. The accuracy rate is measured in the test process of the ML model, and the training process is completed using the dataset of the specified classes.