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논문 기본 정보

자료유형
학술저널
저자정보
Yeon Ji Choi (Kumoh National Institute of Technology) Tariq Rahim (Kumoh National Institute of Technology) Soo Young Shin (Kumoh National Institute of Technology)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.10 No.5
발행연도
2021.10
수록면
390 - 397 (8page)
DOI
10.5573/IEIESPC.2021.10.5.390

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초록· 키워드

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Unmanned aerial vehicles (UAVs) have tremendous potential in civil and public areas. These are especially beneficial in applications where human lives are threatened. Autonomous navigation in unknown environments is a challenging issue for UAVs where decision-based navigation is required. In this paper, a deep learning (DL) approach is presented that aids autonomous navigation for UAVs in completely unknown, GPS-denied indoor environments. The UAV is equipped with a monocular camera and a light detection and ranging (LiDAR) sensor to determine each next maneuver and distance calculation, respectively. For deeper feature extraction, a version of You Only Look Once (YOLOv3-tiny) is improved by adding a convolution layer with different filter sizes. The process is observed as an exercise where the DL model classifies the targeted image as stairs or not stairs. We created our dataset considering the indoor scenario for specific implementation. Comprehensive experimental results are compared with YOLOv3-tiny, indicating better performance in terms of accuracy, recall, F1-score, precision, and maneuvering movements.

목차

Abstract
1. Introduction
2. Related Work
3. The Proposed Scheme
4. Experimental Results and Analysis
5. Conclusion
References

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