Training YOLO v4 on DOTA dataset

The paper focuses on satellite image based object detection. Satellite images-based object detection is being used for an increasingly wide range of tasks. For object detection deep neural networks are used. The training set was created from the DOTA dataset by splitting and scaling the images. As neural network, in this research YOLO architecture is used, more specifically the Yolov4 and Yolov4-tyni configurations. The test set was created from satellite-based images on different scales. The dataset for testing was managed and labelled in CVAT annotation program. In the paper we try to synthesize the results achieved during the training and testing of the mentioned neural networks. Objects are very diverse in size, the recognition rate during testing is greatly influenced by how much the image size is proportional to the size of the images used during training. The images in the testing dataset were scaled to a different size. We tried to evaluate how much the scale of the images and size of the object affect the rate of recognition.