Abstract
Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%
Highlights
The safe operation of high voltage transmission line affects the stability of the power system as main carrier of power grid
The location of the large target is obtained in the unmanned aerial vehicle (UAV) image, and the small target is searched in the large target to determine the pin defect fault in the high-voltage transmission line
Considering that the number of samples in the obtained data set is small, and the types of pin defects are small, this paper combines the idea of transfer learning, and the trained model data is used as the initial weight of the proposed Faster R-CNN model
Summary
The safe operation of high voltage transmission line affects the stability of the power system as main carrier of power grid. In [5], a fault detection method based on fine-grained graphics classification was proposed This method can identify subtle changes due to graphics illumination, colour, shape, and location, and verify the aerial graphics of the transmission network. These methods can accurately identify the objects, they focus on large electric components such as insulators and engineering vehicle, while the researches on small electric components such as pins, screws and bolts, which are very difficult for inspectors, are relatively scarce. The location of the large target is obtained in the UAV image, and the small target is searched in the large target to determine the pin defect fault in the high-voltage transmission line.
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