Method for distinguishing fog concentration in intelligent image monitoring of power transmission line

Abstract

The invention discloses a method for distinguishing fog concentration in intelligent image monitoring of a power transmission line, which adopts a multi-feature synthesis method and comprises the following steps of: extracting a plurality of features related to fog in a picture, and synthesizing the plurality of features extracted from each image into a feature parameter table to form a two-dimensional feature map; utilizing a deep learning convolutional neural network to train and model the characteristic parameter graphs of a large number of sample images; and judging whether the unknown image has fog or not by using the trained model, and distinguishing the concentration levels of the fog, namely clear fog, medium fog and dense fog. According to the method, a plurality of deep and irrelevant features of the provider are integrated to serve as image feature parameters, and then a nonlinear classification algorithm model is trained for a large number of sample images by using the convolutional neural network, so that the problem that the accuracy of detecting whether the image is foggy is low is solved.

Description

technical field [0001] The invention relates to the field of electric power monitoring, in particular to a method for distinguishing fog concentration in intelligent image monitoring of power transmission lines. Background technique [0002] my country's transmission lines are widely distributed, long lines, and complex terrain. Only relying on traditional manual inspection will lead to heavy workload, low efficiency and poor detection effect. [0003] Using computer deep learning technology, intelligent image recognition can be carried out on the captured transmission lines and their surroundings, and it can be analyzed whether there are potential risky objects such as construction machinery, super-high vehicles, floating objects, etc., thus greatly improving the efficiency of line inspections and quality. [0004] At present, the rapid development of industrial technology has brought a serious impact on the climate. Extreme weather such as fog and haze (hereinafter colle...

AI Technical Summary

Problems solved by technology

The correlation between the image brightness feature and the contrast feature described in the above patent is relatively high

The second is that when there are a lot of sample pictures or the relationship between objects in the real world is complex, simply using traditional machine learning algorithms such as Bayesian or SVM is not enough for effective modeling, resulting in low classification accuracy

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