# Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset
## 5 Conclusions  
This study has emphasized that classification problems with a limited size of dataset or without any dataset for training can be solved by generating synthetic data with a game engine. In addition, we have demonstrated that a dataset consisting of fully synthetic images can be used for the training process and such models can be tested on real-image classification.  
In experiments 1 and 2, segmented synthetic data were trained in two different methods: DL networks and a CDNTS layer with DL networks. The models of the two experiments were tested separately with the synthetic-image dataset and the real-image dataset classifications. It was found that the inclusion of the CDNTS layer considerably improved the accuracy performance of the model. In experiment 1, the AUC value of the most successful synthetic-image test result was 74%, and the most successful real-image test result was 72%. In experiment 2, using the CDNTS network, the most successful synthetic image test result AUC value was 89.9%, and the most successful real image test result was 88.9%. The addition of the CDNTS layer to the training and test processes thus increased the accuracy performance.  
The recommended method is limited to images smaller than 20 \(\times\) 20 pixels. In addition, the average time cost for an image for estimating the CDNTS network is 75.9 ms. The method is suitable for applications where the time cost of 75.9 ms is ignored and the image size is larger than 20 \(\times\) 20 pixels. Despite the above-mentioned limitations of our application, the present study has shown that networks can be trained with synthetic images produced by game engines using 3D models to solve real-image classification problems. Thanks to the CDNTS layer, the success of classification networks trained with synthetic data in real-image classification was increased. This study has also shown that using synthetic images is suitable for the purpose of accelerating the solving of image  
Figure 10: Time cost comparison of experiments 1 and 2. Alexnet 55%, squeezenet 74%, and VGG16 70% are time cost of experiment 1. CDNTS + Alexnet 89.9%, CDNTS + squeezenet 88.9%, and CDNTS + VGG16 87.4% are time cost of experiment 2.  
classification problems in which there are insufficient or no image data for training and increasing the performance of testing. At future work, it is aimed to develop a more robust classification model for night vision and thermal vision cameras. In addition, it is aimed to train artificial intelligence with synthetic data generated from just several 2D images.  
Conceptualization, A.E.O.; methodology, A.E.O.; software, A.E.O.; validation, A.E.O. and E.E.; formal analysis, A.E.O. and E.E.; investigation, A.E.O.; resources, A.E.O.; data curation, A.E.O. and E.E.; writing--original draft preparation, A.E.O. and E.E.; writing--review and editing, E.E.; visualization, A.E.O. and E.E.; supervision, A.E.O. and E.E.; project administration, E.E.; funding acquisition, A.E.O. and E.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare that there is no conflict of interests.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UAV & Unmanned aerial vehicle \\ RW-UAV & Rotary-wing UAV \\ FW-UAV & Fixed-wing UAV \\ ML & Machine learning \\ AI & Artificial intelligence \\ DL & Deep learning \\ CNN & Convolutional neural network \\ ILSVRC & IMAGENET Large-Scale Visual Recognition Challenge \\ RELU & Rectified linear unit \\ VGG & Visual geometry group \\
3D & Three-dimensional \\ CDNTS & Corner detection and nearest three-point selection \\
2D & Two-dimensional \\ AUC & Area under the curve \\ GPU & Graphics processing unit \\ CAD & Computer-aided design \\ RGB & Red-green-blue \\ MD5 & Message-digest algorithm 5 \\ SGD & Stochastic gradient descent \\ RMSprop & Root mean square error probability \\ GB & Gigabyte \\ RAM & Random access memory \\ \end{tabular}