Name
Title | ||
|---|---|---|
Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using Uavs |
Abstract | ||
|---|---|---|
The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. Unmanned aerial vehicles (UAVs) present a safer, autonomous, and efficient way to examine the power system components without closing the power distribution system. In this work, a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. A deep Laplacian pyramid-based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low-light images, a low-light image enhancement technique is used for the robust exposure correction of the training images. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. Several flight path strategies are proposed to overcome the shuttering effect of insulators, along with providing a less complex and time- and energy-efficient approach for capturing a video stream of the power system components. The performance of different object detection models is presented for selecting the most suitable one for fine-tuning on the specific faulty insulator dataset. For the detection of damaged insulators, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets and presents a simple and more efficient flight strategy. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust fault recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat, Pakistan. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.3390/s21030974 | SENSORS |
Keywords | DocType | Volume |
primary distribution systems, transfer learning, YoloV4, porcelain insulator detection, UAVs, BRISQUE, LIME, LapSRN, YoloV5 | Journal | 21 |
Issue | ISSN | Citations |
3 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ehab Ur Rahman | 1 | 0 | 0.34 |
| Yihong Zhang | 2 | 9 | 10.65 |
| Sohail Ahmad | 3 | 0 | 0.34 |
| Hafiz Ishfaq Ahmad | 4 | 0 | 0.34 |
| Sayed Jobaer | 5 | 0 | 0.34 |