The object detection in remote sensing imagery based on mosaic improvement

For real-time image recognition, YOLOv4 is currently the most widely used object detection algorithm. However, while the technology is advancing, the problem of improving misjudgment must also be faced at the same time. This study will conduct experiments on remote sensing images, and discuss the effect of data augmentation methods that can improve the amount of data and model performance applied to remote sensing images. Mosaic is a data augmentation method proposed by YOLOv4. Mosaic mainly stitches pictures together so that the model can learn multiple pictures at once and improve training performance. However, because of the way the pictures are synthesized, the interaction between the object and the background makes Mosaic unable to fully play its role probably. Therefore, this study will also explore the different effects of subsequent training and detection caused by differences in stitching methods, and how objects in remote sensing images may not be successfully identified, and propose a data augmentation method to solve the above issues.