Name
Title | ||
|---|---|---|
Engineering Vehicles Detection Based on Modified Faster R-CNN for Power Grid Surveillance. |
Abstract | ||
|---|---|---|
Engineering vehicles intrusion detection is a key problem for the security of power grid operation, which can warn of the regional invasion and prevent external damage from architectural construction. In this paper, we propose an intelligent surveillance method based on the framework of Faster R-CNN for locating and identifying the invading engineering vehicles. In our detection task, the type of the objects is varied and the monitoring scene is large and complex. In order to solve these challenging problems, we modify the network structure of the object detection model by adjusting the position of the ROI pooling layer. The convolutional layer is added to the feature classification part to improve the accuracy of the detection model. We verify that increasing the depth of the feature classification part is effective for detecting engineering vehicles in realistic transmission lines corridors. We also collect plenty of scene images taken from the monitor site and label the objects to create a fine-tuned dataset. We train the modified deep detection model based on the technology of transfer learning and conduct training and test on the newly labeled dataset. Experimental results show that the proposed intelligent surveillance method can detect engineering vehicles with high accuracy and a low false alarm rate, which can be used for the early warning of power grid surveillance. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18072258 | SENSORS |
Keywords | Field | DocType |
power grid surveillance,external damage,engineering vehicles,faster R-CNN,transfer learning | Electronic engineering,Power grid,Engineering | Journal |
Volume | Issue | Citations |
18 | 7.0 | 0 |
PageRank | References | Authors |
0.34 | 9 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-Zhi Xiang | 1 | 12 | 7.35 |
| Ning Lv | 2 | 31 | 11.32 |
| Xinli Guo | 3 | 0 | 0.34 |
| Shuai Wang | 4 | 252 | 48.81 |
| Abdulmotaleb El-Saddik | 5 | 2416 | 248.48 |