Many different disciplines use deep learning algorithms for various purposes. In recent years, object detection by deep learning from aerial or terrestrial images has become a popular research area. In this study, object detection application was performed by training the YOLOv2 and YOLOv3 algorithms in the Google Colaboratory cloud service with the help of Python software language with the DOTA dataset consisting of aerial photographs. 43 aerial photographs containing 9 class objects were used for evaluation. These classes are large vehicle, small vehicle, plane, harbor, storage tank, ship, basketball court, tennis court and swimming pool. Accuracy analyzes of these two algorithms were made according to recall, precision and F1-score for nine classes, and the results were compared accordingly. YOLOv2 gave better results in 5 out of 9 classes, while YOLOv3 gave better results in recognizing small objects. While the best result with YOLOv2 was obtained in airplane class with 99% F1-score, the best result with YOLOv3 was obtained in swimming pool class with 83%. YOLOv2 can detect objects in an average photograph in 43 seconds, YOLOv3 has achieved superior performance in terms of time by detecting objects in an average of 2.5 seconds.