Name
Abstract | ||
|---|---|---|
A power substation is susceptible to intrusions of foreign objects. The intrusions can likely result in failures of power supplies. Therefore, recognizing foreign objects becomes important to ensure constant and stable power supplies. However, existing object recognition methods fail to achieve acceptable accuracy and performance. In this paper, we propose an efficient Foreign Objects Detection Network for Power Substation (FODN4PS) to improve the recognition accuracy with less time. FODN4PS consists of a Moving Object Region Extraction Network (MORE Net) and a classification network, where the MORE Net can get the position of foreign objects, and the classification network can recognize the category of foreign objects. Experimental results show that FODN4PS is faster and more accurate in object recognition than the Fast R-CNN and Mask R-CNN. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.imavis.2021.104159 | Image and Vision Computing |
Keywords | DocType | Volume |
Power substation,Deep learning,Foreign objects detection,FODN4PS | Journal | 109 |
ISSN | Citations | PageRank |
0262-8856 | 0 | 0.34 |
References | Authors | |
29 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Liang Xu | 1 | 57 | 14.47 |
| Yongkang Song | 2 | 0 | 0.34 |
| Weishan Zhang | 3 | 31 | 5.55 |
| Yunyun An | 4 | 0 | 0.34 |
| Ye Wang | 5 | 0 | 0.34 |
| Huansheng Ning | 6 | 847 | 83.48 |