Deep learning-based multi-target locating method for power device infrared image

Abstract

The invention discloses a deep learning-based multi-target locating method for a power device infrared image. The method comprises the steps of 1) obtaining the standard power device infrared image through a substation device detection apparatus; 2) establishing a power device infrared image sample library, and extracting a training set, a verification set and a test set; 3) establishing a FASTER-RCNN deep target detection neural network, training the established FASTER-RCNN deep target detection neural network by using the training set of the sample library, and verifying the over-fitting degree of a model by using the verification set; and 4) by utilizing the network model built by training, performing multi-target identification and locating on the infrared image in the test set, and generating an identification result. According to the method, the input infrared image is subjected to deep feature mining by utilizing a deep learning algorithm, without depending on manual extractionof feature parameters, and the regions and positions of various power main devices can be effectively and accurately identified, so that the labor amount is reduced to a certain extent.

Description

technical field [0001] The invention relates to the technical field of infrared image recognition and positioning of power equipment, in particular to a method for multi-target positioning of infrared images of power equipment based on deep learning. Background technique [0002] Infrared images of power equipment are detected by infrared technology through infrared radiation energy emitted by power equipment, and converted into corresponding electrical signals, and the thermal image of the surface of power equipment is obtained after electrical signal processing. Infrared detection technology has the characteristics of long-distance, non-contact, non-sampling, non-disintegration, accurate, fast, and intuitive. It is widely used in the detection and diagnosis of overheating defects in power equipment, and is of great significance to improving the stability of power systems. Therefore, efficiently and accurately identifying the area and location of the power backbone equipmen...

AI Technical Summary

Problems solved by technology

Compared with the traditional shallow neural network, deep learning has a series of hidden layers capable of nonlinear transformation, which can challenge more complex environments and problems

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