Distribution Line Pole Detection and Counting Based on YOLO Using UAV Inspection Line Video
| Autor: | Xiren Miao, Binghuang Chen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 020208 electrical & electronic engineering ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing 02 engineering and technology Convolutional neural network Object detection Electric power transmission Transmission line Minimum bounding box Line (geometry) 0202 electrical engineering electronic engineering information engineering Overhead (computing) 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business |
| Zdroj: | Journal of Electrical Engineering & Technology. 15:441-448 |
| ISSN: | 2093-7423 1975-0102 |
| DOI: | 10.1007/s42835-019-00230-w |
| Popis: | In order to improve the efficiency of post-disaster treatment of power distribution network, the application of UAV in disaster reduction and relief has been paid much attention by the power sector. Aiming at the loss assessment needs of overhead transmission lines in distribution network, this paper proposes an innovative solution of pole detection and counting in distribution network based on UAV inspection line video. Combined with the characteristics of YOLO’s rapid detection, the convolution neural network is applied to the image detection of the pole state. In addition, the pole data and corresponding images are obtained at the same time of detecting the inspection line video. Therefore, the power department can quickly count the losses to cope with the disaster. The anchor value is modified before image training by YOLO v3, and sets the corresponding ROI for the UAV inspection line standard. In order to quickly obtain the loss assessment of post-disaster pole lodging, this paper proposes a counting algorithm by using the continuous ordinate change of the bounding box of the same pole in front and rear frame of video, so that the classified counting of pole is accurate and the detection precision is above 0.9. The results obtained in video test show that this method is effective in detecting and counting the state of the pole of overhead transmission line in distribution network. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink