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| Title | Real-Time Vision-Based River Detection and Lateral Shot Following for Autonomous UAVs |
| Creator | 劉吉軒 Jyi-Shane Liu Huang, Yenting Lee, Gongyi Soong, Rutai |
| Contributor | 資科系 |
| Key Words | Rivers ; Image segmentation ; Unmanned aerial vehicles ; Navigation ; Inspection ; Task analysis ; Image edge detection |
| Date | 2020-09 |
| Date Issued | 4-Jun-2021 14:50:15 (UTC+8) |
| Summary | 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 |
| Type | conference |
| DOI | https://doi.org/10.1109/RCAR49640.2020.9303263 |
| dc.contributor | 資科系 | |
| dc.creator (作者) | 劉吉軒 | |
| dc.creator (作者) | Jyi-Shane Liu | |
| dc.creator (作者) | Huang, Yenting | |
| dc.creator (作者) | Lee, Gongyi | |
| dc.creator (作者) | Soong, Rutai | |
| dc.date (日期) | 2020-09 | |
| dc.date.accessioned | 4-Jun-2021 14:50:15 (UTC+8) | - |
| dc.date.available | 4-Jun-2021 14:50:15 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Jun-2021 14:50:15 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/135538 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 1412871 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Proceedings of the 2020 IEEE International Conference on Real-time Computing and Robotics, IEEE | |
| dc.subject (關鍵詞) | Rivers ; Image segmentation ; Unmanned aerial vehicles ; Navigation ; Inspection ; Task analysis ; Image edge detection | |
| dc.title (題名) | Real-Time Vision-Based River Detection and Lateral Shot Following for Autonomous UAVs | |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1109/RCAR49640.2020.9303263 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/RCAR49640.2020.9303263 |