# A Remote Sensing Image Target Detection Algorithm Based on Improved YOLOv8
## 4 Experimentation and Analysis  
### Dataset and Experimental Environment  
In this paper, we choose to use Dataset for Object Detection in Aerial Images (DOTA) [22] to train and evaluate our improved model. DOTA is a large image dataset for target detection in aerial images released by Wuhan University in 2017, including three versions: DOTAv1.0, DOTAv1.5, and DOTAv2.0. The DOTAv1.0 version is used in this experiment, which contains a total of 2806 aerial images with resolutions ranging from \(800\) pixels \(\times\)\(800\) pixels to \(4000\) pixels \(\times\)\(4000\) pixels, including 188,282 annotations for 15 types of remote sensing feature targets: plane, ship, storage tank, baseball diamond, tennis court, basketball court, ground track field, harbor, bridge, large vehicle, small vehicle, helicopter, roundabout, soccer ball field, and swimming pool. Compared with other publicly available remote sensing datasets, DOTA v1.0 contains more remote sensing images with small and multi-sized targets, and there are enough samples in each category available for training. Therefore, we selected the DOTA v1.0 dataset to train our network so that the model can achieve improved effectiveness on the remote sensing target detection task. In this experiment, six categories of targets with the highest numbers of labels in the DOTAv1.0 dataset, i.e., small vehicle, large vehicle, plane, ship, storage tank, and harbor, are selected as the targets to be detected; the number and size of each category is shown in Figure 8. These six categories of targets are distributed in different remote sensing backgrounds, and many of them account for a small proportion of the pixels in the image, which can fully reflect the effect of the improved algorithm proposed in this paper on the detection of targets with different levels of background complexity, multiple sizes, and small sizes.  
Figure 8: The number and size distribution for each category.  
The original image size of the DOTA dataset is large, and using the images directly for training can lead to an excessive amount of computational parameters. Therefore, before training, the original image size is uniformly reduced to 640 pixels \(\times\) 640 pixels by pixel transformation. In addition to this, we also use mosaic image enhancement on the input images. The mosaic algorithm randomly scales, crops, arranges, and splices four images into a new image for model training, which is equivalent to a lossless improvement of the training epoch and reduces the consumption of graphics card memory while improving the training effect. At the same time, the large targets in the dataset are also randomly scaled to small targets, and the size of the small targets in the original image are scaled to smaller sizes, which increases the number of small targets, realizes the improvement of the diversity of the training data, and improves the model's robustness.  
The experiments are based on the PyTorch deep learning framework, and a single NVIDIA GeForce GPU 3090 graphics card is used for model training. The specific configuration of the experimental environment is shown in Table 1.