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.
초록·키워드
Mobile robots operating in disaster environments frequently transition between indoor and outdoor spaces, where abrupt changes in illumination, background appearance, and structural patterns occur. These domain transition regions often lead to unstable frame-level environmental perception. This study analyzes the visual uncertainty and decision fluctuations observed in such regions and proposes a sequence-based approach for stable domain transition recognition. A video dataset containing domain transitions is constructed, and 30 visual features—including classification probabilities, entropy, brightness variation, edge density, structural cues, and motion information—are extracted. SHAP-based feature importance analysis is employed to identify the top 14 key features that most significantly contribute to transition region discrimination. Using these selected features, sequence-based classification models based on BiLSTM, TCN, and Tiny Transformer are trained. In addition, EMA- and HMM-based stabilization techniques are applied to suppress decision fluctuations in transition regions. The experimental results show that sequence-based models outperform frame-level classification in transition detection accuracy and decision stability. In particular, the combination of TCN and HMM achieves the best overall performance. These findings demonstrate that integrating sequence modeling with stabilization techniques is an effective strategy for recognizing domain transition regions characterized by gradual visual changes, thereby improving the real-time environmental perception stability of disaster response robots
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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UCI(KEPA) : I410-151-26-02-096087253
