Multifunctional electric power image intelligent analysis device based on AI deep learning

Abstract

The invention discloses a multifunctional electric power image intelligent analysis device based on AI deep learning. The multifunctional electric power image intelligent analysis device comprises an image acquisition module, an image noise reduction module, an image preprocessing module, a region growing module, a gray mapping enhancement contrast module, a target image recognition module, an image feature extraction and recognition module, an equipment recognition module and a comparative analysis module. The region growing module is used for growing each core region phi i to obtain a control region corresponding to each core region phi i, and the gray mapping enhancement contrast module is used for sequentially mapping the initial gray value of each pixel point in each core region phi i into a first intermediate gray value to enhance the region contrast. According to the device, the contrast ratio of the electric power image can be improved, the edge information and detail information of the electric power image can be effectively protected, the collected image is effectively enhanced, the image quality is improved, and the recognition accuracy of the electric power image is improved.

Description

technical field [0001] The present invention relates to the technical field of power systems, in particular to a multifunctional power image intelligent analysis device based on AI deep learning. Background technique [0002] With the development and promotion of artificial intelligence technology, the research of image recognition method using deep learning in power fault diagnosis is gradually attracting attention. Different from traditional machine learning, deep learning requires a large number of positive samples for training, so that the machine can learn enough features to achieve inferences from one instance, so as to prevent the phenomenon of overfitting that is prone to occur when the data set is too small. However, since the promotion and application of helicopters, drones and intelligent robots is not long, the amount of image data collected for some fault types is small, which cannot meet the needs of deep learning. [0003] Due to the influence of many factors...

AI Technical Summary

Problems solved by technology

However, since the promotion and application of helicopters, drones and intelligent robots is not long, the amount of image data collected for some fault types is small, which cannot meet the needs of deep learning.

[0003] Due to the influence of many factors such as the imager, imaging environment, and the characteristics of the imaging object itself, there are often differences between the captured device image and the original device, resulting in a significant decline in the quality of the obtained image, and poor image quality will increase the quality of the image processing process. Useless information in , reduce useful information, and ultimately affect the recognition results

Images