Name
Abstract | ||
|---|---|---|
This paper proposes an insulator defect detection algorithm based on computer vision for helicopter aerial insulator imaging in complex backgrounds. The algorithm runs fast with high detection accuracy, which meets the requirements for detecting missing insulators. However, because the background of the insulator image acquired by aerial photography is complicated and there is more than one insulator type, defects are difficult to detect. Therefore, the first step is to obtain a classifier that can identify and locate the insulator by feature extraction and training, after which the insulator is segmented by a series of digital image processing methods. Finally, the pixels obtained by segmenting the insulator can be analyzed to determine whether the insulator is missing. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICInfA.2017.8078934 | 2017 IEEE International Conference on Information and Automation (ICIA) |
Keywords | Field | DocType |
Feature extraction,Classifier,Digital image processing,Pixel statistics | Histogram,Computer vision,Feature detection (computer vision),Computer science,Algorithm,Feature extraction,Image segmentation,Pixel,Artificial intelligence,Digital image processing,Classifier (linguistics),Insulator (electricity) | Conference |
ISBN | Citations | PageRank |
978-1-5386-3155-3 | 0 | 0.34 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zuo Dan | 1 | 0 | 0.34 |
| Hu Hong | 2 | 2 | 5.31 |
| Qian Ronghui | 3 | 0 | 0.34 |
| Ze Liu | 4 | 9 | 3.96 |