Mask R-CNN을 이용한 이미지 합성 기반 데이터증강의 자주포 객체탐지 모델 성능향상 연구 = Image Synthesis-Based Data Augmentation using Mask R-CNN Self-Propelled Artillery Object Detection Model Performance Improvement Study

다국어 초록 (Multilingual Abstract)

The applications of AI technology have limitations in defense, such as the difficulty in obtaining sufficient high-quality big data and the lack of diversity in the acquired data. This study evaluated image data augmentation methods suitable for military operational environments to overcome these issues.
First, the object selected is the "self-propelled artillery." By separating the object and synthesizing it into a specific background, an image synthesis-based data augmentation method is proposed to address the lack of data for military operational environments. Self-propelled artillery images identified in winter were used as test data to verify the performance. Three methods are used to augment the data: the baseline data augmentation method using self-propelled artillery training data identified in non-winter environments, data augmentation using Cycle-GAN, one of the image generation techniques, and the proposed image synthesis-based data augmentation method. The object detection performance was compared using the YOLOv5 model.
The results show that using the image synthesis-based data augmentation method with the augmented training dataset achieved the highest performance, with an mAP (0.5) of 97%. This study shows that image synthesis-based data augmentation can address the lack of diversity in defense sector data. In addition, it provides a method to improve object detection model performance in weapon system detection.