EpNet : Power lines foreign object detection with Edge Proposal Network and data composition
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Article number | 108857 |
Journal / Publication | Knowledge-Based Systems |
Volume | 249 |
Online published | 27 Apr 2022 |
Publication status | Published - 5 Aug 2022 |
Externally published | Yes |
Link(s)
Abstract
Power lines foreign object detection task is to detect objects suspending on power lines, they might be kites, plastic bags or anything else, which could be a potential risk to power system. However, this task remains a challenge due to the lack of data, because these data can only be produced by a few major video surveillance companies, who treat their data as valuable property and will not share it with others. Without massive training data, we could not obtain an excellent neural network. In this paper, we introduce a new data composition method to generate artificial data and help alleviate the data shortage problem. What is more, we propose a new detection method called Edge Proposal Network (EpNet) to reduce wrong proposal locations and increase detection performance. At last, we conduct several experiments to verify the effectiveness of the two methods, and some discussion experiments to gain a deeper understanding of the composited data.
Research Area(s)
- Data composition, Foreign object detection, Power lines
Citation Format(s)
EpNet: Power lines foreign object detection with Edge Proposal Network and data composition. / Su, Junyu; Su, Yukun; Zhang, Yu et al.
In: Knowledge-Based Systems, Vol. 249, 108857, 05.08.2022.
In: Knowledge-Based Systems, Vol. 249, 108857, 05.08.2022.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review