인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
Infrared images are known to capture the thermal radiation emitted from objects and are increasingly essential in Night Vision and surveillance. These can be utilized in various image processing algorithms, such as object detection and tracking. However, infrared image processing is highly complex due to the sensor degradation and the status of temperature inversion between the background and the object, which results in an inadequate dataset. Data augmentation approaches have been introduced to overcome the lack of datasets by increasing the diversity of data distribution. Withal, the augmentation approach via image processing algorithms is widely used to improve model performance, prevent overfitting caused by insufficient data, and mitigate data bias. Furthermore, several recent studies have established novel algorithms to overcome dataset shortage and uniform distribution through domain shifts such as image generation and image-to-image translation. In this paper, the object detection performance with infrared data augmentation based on the diffusion models of "Palette" and "BBDM" are analyzed and evaluated from various perspectives, such as the number of images, class, and object size. The evaluation showed that the compound dataset of Palette and BBDM at the ratio of 20% and 10%, respectively, improved by 0.3% and 0.5% 0.5 mAP compared to the baseline. Nevertheless, the similar distribution of real and translated infrared images showed better qualitative and quantitative performances.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
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