Detection method and system of power equipment based on multispectral image

A detection method of power equipment based on a multispectral image, comprising at least the following steps:

• step S10:
• obtaining an image of power equipment to be detected, wherein the image is one of an infrared image, an ultraviolet image, and a visible image;
• step S11:
• inputting the image into a pre-trained pixel-based power equipment detection model for detection, and performing classified prediction on pixels in the image to obtain a predicted result, wherein the power equipment detection model comprises at least a trunk feature extraction unit, a feature integration processing unit, an attention adaptive processing unit, and a prediction and conversion unit;
• and
• step S12:
• outputting a predicted image based on the predicted result, wherein the predicted image is power equipment image with background information removed, and is marked with a name of each piece of equipment,
• wherein step S11 further comprises:
• step S110:
• converting the image into a predetermined size, and extracting a predetermined number of classes of preliminary effective features in the image by using the trunk feature extraction unit;
• step S111:
• upsampling the predetermined number of classes of preliminary effective features, and performing feature integration to obtain an integrated-feature layer;
• step S112:
• processing the integrated-feature layer by using the attention adaptive processing unit to obtain a processed adaptive integrated-feature layer;
• step S113:
• predicting the processed adaptive integrated-feature layer to obtain a result of classified prediction of the pixels in the image;
• and
• step S114:
• converting, based on the result of classified prediction of the pixels, gray levels of background pixels of the pixels into a predetermined value,
• wherein the attention adaptive processing unit further comprises a channel attention processing unit, a spatial attention processing unit, and a weighting processing unit, and step S112 further comprises:
• step S1120:
• inputting the integrated-feature layer into the channel attention processing unit for processing to obtain a channel attention weight of each channel of the integrated-feature layer, and weighting the integrated-feature layer by using the channel attention weight to obtain a channel integrated-feature layer;
• step S1121:
• inputting the integrated-feature layer into the spatial attention processing unit for processing to obtain a spatial attention weight of each feature point of the integrated-feature layer, and weighting the integrated-feature layer by using the spatial attention weight to obtain a spatial integrated-feature layer;
• and
• step S1122:
• weighting each feature in the channel integrated-feature layer and the spatial integrated-feature layer by using the following formula based on a variable coefficient to obtain an adaptive integrated-feature layer,
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• wherein sp(x) is a feature value of the channel integrated-feature layer, ch(x) is a feature value of the spatial integrated-feature layer, g(x) is a feature value of the adaptive integrated-feature layer, and a is the variable coefficient.