Name
Title | ||
|---|---|---|
Deep-learning-based autonomous navigation approach for UAV transmission line inspection. |
Abstract | ||
|---|---|---|
This paper aims to resolve the problem of UAV robustly continuous navigation along one side of overhead transmission lines. To this end, we develop a corresponding navigation scheme and address the following three key issues. First, we integrate the tracking and deep-learning-based detection for the real-time and reliable transmission tower localization. Second, to provide UAV with a robust and precise heading, we compute and optimize the vanishing point of transmission lines. Third, to solve the problem of measurement of distance from transmission lines, we turn to estimate the distance from UAV to transmission tower by triangulation following the multiple-view strategy. Finally, by the designed UAV platform, the whole system is tested in a practical field environment and achieves an encouraging result. To the best of our knowledge, this paper marks the first time that a continuous flight scheme a long o ne s ide o f transmission lines is put forward and well implemented. |
Year | Venue | Field |
|---|---|---|
2018 | ICACI | Tower,Transmission line,Computer science,Transmission tower,Robustness (computer science),Real-time computing,Electric power transmission,Triangulation (social science),Artificial intelligence,Deep learning,Vanishing point |
DocType | Citations | PageRank |
Conference | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoguang Zhao | 1 | 54 | 18.68 |
| Min Tan | 2 | 2342 | 201.12 |
| Xiaolong Hui | 3 | 1 | 1.04 |
| Jiang Bian | 4 | 897 | 61.74 |