Power transmission line equipment defect detection method based on sample offset network

Abstract

The invention discloses a power transmission line equipment defect detection method based on a sample offset network, and the method comprises the steps: transmitting a data set into a designed convolutional neural network model for training, and completing the parameters of the convolutional neural network model; and deploying the trained convolutional neural network model to detection equipment, and detecting equipment defects of the power transmission line. A mosaic data enhancement method is used for processing data, images lacking samples can be supplemented, meanwhile, background information of defect categories is enriched, and the situation of network over-fitting is reduced. Through a feature extraction and feature fusion module, the diversity of the input image is enriched, so that the network can accurately judge the region of interest, and the detection capability of the network is enhanced. By correcting the position of a candidate box, the classification task and the regression task obtain different candidate areas. The number of actions identified by the identification method has expandability, and the expansion operation is simple and easy for developers to operate.

Description

technical field [0001] The invention relates to a defect detection method for transmission line equipment based on a sample offset network, and belongs to the technical fields of image data processing and neural network in the field of artificial intelligence. Background technique [0002] Due to the increasing scale of my country's power grid, the quantity and quality of power on-site inspections have higher requirements for power personnel. However, the environment of the power transmission site is complex, with a variety of equipment distributed, and it is difficult to accurately observe the equipment connected by various overhead lines. , to ensure the stable operation of the transmission line, all equipment needs to work together normally. The initial fault inspection method mainly relied on on-site staff to conduct manual analysis and diagnosis based on manual inspection experience. This method has a high level of professional requirements for the staff, and is often ac...

AI Technical Summary

Problems solved by technology

The initial fault inspection method mainly relied on on-site staff to conduct manual analysis and diagnosis based on manual inspection experience. This method has a high level of professional requirements for the staff, and is often accompanied by shutdown inspections, which consumes a lot of manpower, material and financial resources, takes a long time, and is dangerous. Highly sexual and easily influenced by personal experience

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