An optimized YOLOv8-based algorithm for remote sensing image detection

Target detection technology in remote sensing images holds significant importance in fields such as signal processing and radar imaging. However, due to the substantial influence of electromagnetic wave scattering characteristics on remote sensing image, the size of objects to be detected undergoes considerable changes, increasing the difficulty of detection. At the same time, the extensive background information contains confusing geographical elements, severely affecting the detection performance. We use YOLOv8 as the baseline model and employ Swin Transformer as the backbone structure to enhance the model's ability to connect context. Additionally, Coordinate Attention is added at the end of the backbone to deal with complex backgrounds, concentrating more weight on our points of interest. Finally, to detect smaller targets, we incorporate a smaller scale detection head to capture more subtle points of interest. Through experiments, our model's [email protected], Recall, and Precision reached 0.628, 0.583, and 0.691, respectively, far surpassing several existing advanced models.