Power line inspection image automatic identification method based on neural network and power line inspection image automatic identification device thereof

Abstract

The invention discloses a power line inspection image automatic identification method based on a neural network and a power line inspection image automatic identification system thereof. The method comprises the steps that input images acquired in power line inspection are acquired, and the input images are inputted to a convolutional neural network; 2) a feature vector group is obtained, and the input images apply the feature vectors in the feature vector group in turn so that digital description of the select box of the target type and the target position is obtained; and 3) convolutional operation is performed on the target input image so that the select box of the target object and the target position is obtained, and the object and the position expected to be selected of the highest confidence are also obtained. An end-to-end training model is established so that the target object in the images can be automatically identified, power line inspection image automatic identification can be realized based on the neural network and the efficiency can be enhanced.

Description

technical field [0001] The invention relates to the fields of power line inspection and image processing, in particular to a neural network-based automatic recognition method and device for power line inspection images. Background technique [0002] Due to the continuous expansion of the domestic power grid, long-distance transmission lines, such as UHV lines, have grown rapidly. However, many transmission lines are distributed between mountains and mountains, which leads to the traditional manual line inspection being affected by uncertain factors such as terrain environment, personnel quality, and weather conditions. The efficiency is low, the re-inspection period is long, and the accuracy of inspection data is not high. Therefore, in recent years, China has begun to gradually develop helicopter intelligent inspection technology, which can not be restricted by the geographical environment and greatly improves the efficiency. At the same time, due to the unique vision of t...

AI Technical Summary

Problems solved by technology

The main disadvantage of these algorithms is that the discrimination method is too simple but the calculation logic is complex, resulting in poor recognition accuracy, and the calculation speed is slow (as the size of the picture increases and the color difference decreases, the running time of the algorithm increases exponentially, but the accuracy rate drops sharply. )

Moreover, it is difficult to significantly improve efficiency by using the above algorithm, and it is difficult to achieve breakthroughs in reducing calculation time or improving accuracy.

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