  Data augmentation refers to the process of applying a series of
transformations or expansions to original data to generate new samples, thereby
increasing the diversity and quantity of the data, effectively improving the
performance and robustness of models. As a common data augmentation method,
Mosaic data augmentation technique stitches multiple images together to
increase the diversity and complexity of training data, thereby reducing the
risk of overfitting. Although Mosaic data augmentation achieves excellent
results in general detection tasks by stitching images together, it still has
certain limitations for specific detection tasks. This paper addresses the
challenge of detecting a large number of densely distributed small objects in
aerial images by proposing the Select-Mosaic data augmentation method, which is
improved with a fine-grained region selection strategy. The improved
Select-Mosaic method demonstrates superior performance in handling dense small
object detection tasks, significantly enhancing the accuracy and stability of
detection models. Code is available at
https://github.com/malagoutou/Select-Mosaic.
