Improvement of Object Detection Performance for Enemy Tanks Using the Combination of Data Augmentation Models
Kyo-Seong Hwang, Jungmok Ma · Korean Journal of Computational Design and Engineering · 2022
With the development of artificial intelligence technology, it is continuously being applied in the defense field. In particular, deep learning-based object detection technology is treated as a groundbreaking technology in the field of defense surveillance and reconnaissance. However, there are limitations to use deep learning-based object detection technology because it is hard to get enough image data of enemy weapon systems. To overcome this challenge, this paper studies the improvement of object detection performance for enemy tanks using the combination of data augmentation models. Experiment results show that the combination of selected data augmentation models improves object detection performance(especially SinGAN model is effective). The result indicates that the data augmentation in the field of defense surveillance and reconnaissance needs to be studied since the result of combining all data augmentation models would not necessarily be good.