A Bird's Nest Recognition Method for Transmission Lines Based on Statistical Features and Machine Learning

Abstract

The invention relates to a bird's nest identification method for transmission lines based on statistical features and machine learning, including: classifying according to whether there are bird's nests in the transmission line in the image, and specifying that images without bird's nests are positive samples, and images containing bird's nests are negative samples Sample; convert the image of the training set from the RGB color space to the HSV color space; decompose in the HSV color space, and obtain the H, S, V three channel color components of the power line image, on the H, S, V three channels Extract its statistical features separately, count the first-order moment to the sixth-order moment, and form these features into an n×18-dimensional data set, where n represents the number of samples; train the classifier; use the trained classifier to recognize the power line image Identify and determine whether there is a bird's nest.

Description

technical field [0001] The invention belongs to the fields of electric power technology and computer vision, and relates to a method for identifying a bird's nest of a power transmission line based on statistical features and machine learning. Background technique [0002] Transmission lines are an indispensable part of the power grid. Its main function is to transmit, distribute and exchange electrical energy. At the same time, it also has a more important role to connect several independent grids to form an interconnected grid or the same grid, thereby improving the desirability of safe power supply in the power system. It is directly related to the electricity consumption problem of the people, and a large-scale power outage will bring huge losses to the social economy. Therefore, the safety of transmission lines is one of the issues of great concern to the power sector. [0003] Most of the transmission lines run in the wilderness, and the surrounding environment is v...

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Problems solved by technology

[0009] However, the above-mentioned system requires long-term monitoring by the staff, which is greatly affected by human factors. Long-term work will inevitably make it difficult for the staff to concentrate for a long time, and the work efficiency will decrease; in addition, if the subjective judgment of the staff is used for these data , it is very easy to misjudgment or miss judgment, it is difficult to accurately find the potential safety hazards of power transmission equipment, and it greatly increases the maintenance cost, which cannot meet the needs of smart grid construction

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