政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/135538

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113311/144292 (79%)
Visitors : 50933838 Online Users : 942

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 資訊學院 > 資訊科學系 > 會議論文 > Item 140.119/135538

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/135538

Title:	Real-Time Vision-Based River Detection and Lateral Shot Following for Autonomous UAVs
Authors:	劉吉軒 Jyi-Shane Liu Huang, Yenting Lee, Gongyi Soong, Rutai
Contributors:	資科系
Keywords:	Rivers;Image segmentation;Unmanned aerial vehicles;Navigation;Inspection;Task analysis;Image edge detection
Date:	2020-09
Issue Date:	2021-06-04 14:50:15 (UTC+8)
Abstract:	Most existing autonomous UAV inspection tasks focus on environment surroundings and facilities. The UAV often navigates above the inspected target and conducts inspection with the camera aiming downward on the target. However, in some scenarios, it is risky to allow UAVs to navigate above the inspected target. For example, when patrolling a river, the UAV may risk falling into the river. Similar risks also exist for scenarios such as railways and power lines. This research proposes a lateral shot following approach for UAVs to follow the river laterally while collecting image data with a front view camera. The proposed approach has been evaluated with different segments of river in real world environments. The experiments include two types of following method and two types of viewpoint to suit different task needs. Results show that our deep neural network can extract the river masks in real-time with high accuracy. With adaptive steering adjustments, the UAV can achieve accurate and robust following when handling geographical change of river segments. Performance comparison between human operators and our developed autonomous system shows that better following accuracy and consistency can be achieved by our autonomous system.
Relation:	Proceedings of the 2020 IEEE International Conference on Real-time Computing and Robotics, IEEE
Data Type:	conference
DOI 連結:	https://doi.org/10.1109/RCAR49640.2020.9303263
DOI:	10.1109/RCAR49640.2020.9303263
Appears in Collections:	[資訊科學系] 會議論文

Files in This Item:

File	Description	Size	Format
294.pdf		1379Kb	Adobe PDF2	283	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...