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
Abstract This paper presents a robust approach to address the critical challenge of self-localization for flying vehicles, especially in environments with noisy data or an absence of GPS signals. Our proposed solution uses the power of deep learning, specifically Convolutional Neural Networks (CNNs), to accurately determine a vehicle’s geographic coordinates (latitude and longitude) by analysing satellite imagery. To train and validate our system, we have compiled a comprehensive dataset of Google Maps covering Egypt, ensuring a wide variety of visual terrains and features. The methodology leverages the inherent strengths of CNN architectures to efficiently process large-scale visual datasets while automatically identify and extract significant topographical features. This autonomous feature-extraction capability is crucial for real-world satellite operations, where conditions are often unpredictable and data can be imprecise. By employing a transfer learning strategy, we have adapted two powerful, pre-trained CNN models: SqueezeNet and GoogLeNet. These networks were fine-tuned using our custom Google Maps imagery dataset, enabling them to effectively learn and generalise from the distinct visual attributes present in the maps. The resulting system offers a reliable and precise localization alternative, proving essential for the consistent operation of autonomous systems in GPS-denied or challenging environments. The resulting system predicts position accurately, achieving an accuracy of 99.42% and 98.9% for GoogLeNet and SqueezeNet, respectively, for case 1(zoom level 9) images with a resolution of 5.6 km × 5.6 km. While the grayscale version for the same case gives 95.5% for GoogLeNet, and 96% for SqueezeNet. For case 2, zoom scale 11, where the network achieves a resolution of 395 m × 395 m, GoogLeNet reaches an accuracy of 97.7%, and SqueezeNet reaches an accuracy of 93%. Although the grayscale version results in 62% for GoogLeNet and 67% for SqueezeNet.
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.
최근 본 자료 전체보기
