DETECTION METHOD AND SYSTEM OF POWER EQUIPMENT BASED ON MULTISPECTRAL IMAGE

The present disclosure discloses a detection method and system of power equipment based on a multispectral image, and relates to the field of smart grid information technology. The method includes: obtaining an image of power equipment to be detected, where the image is one of an infrared image, an ultraviolet image, and a visible image; 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; and outputting a predicted image based on the predicted result, where the predicted image is power equipment image with background information removed, and is marked with a name of each piece of equipment. With the implementation of the present disclosure, efficiency and accuracy of power equipment detection can be improved.

CROSS REFERENCE TO RELATED APPLICATION

The present application claims priority to the Chinese Patent Application No. 202211463040.0, filed with the China National Intellectual Property Administration on Nov. 21, 2022, and entitled “DETECTION METHOD AND SYSTEM OF POWER EQUIPMENT BASED ON ATTENTION ADAPTATION OF MULTISPECTRAL IMAGE”, which is incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to the field of smart grid information technology, and in particular, to a detection method and system of power equipment based on a multispectral image.

BACKGROUND

Currently, infrared, ultraviolet and visible light detection for power equipment is completely performed manually, which has a huge workload of analysis and processing, and has high requirements on professionalism and work experience of inspectors, and detection results are subjective to a certain degree. With the development of artificial intelligence technology, in the future, the infrared, ultraviolet and visible light detection technology should develop in the direction of intelligent identification and analysis, to form an accurate evaluation system and establish a standardized management platform so as to improve support for power equipment state evaluation management.

Since there are many types of power equipment and their structures are complex, the premise of power equipment state evaluation is power equipment type identification and detection of regional key information. However, existing infrared, ultraviolet and visible light detection has the problems of heavy manual workload and insufficient accuracy.

SUMMARY

Against the technical problems to be solved, the present disclosure provides a detection method and system of power equipment based on a multispectral image, which can improve efficiency and accuracy of power equipment detection.

To solve the technical problems, in an aspect of the present disclosure, a detection method of power equipment based on a multi-spectral image is provided, including at least the following steps:

Preferably, the attention adaptive processing unit further includes a channel attention processing unit, a spatial attention processing unit, and a weighting processing unit, and step S112 further includes:

Preferably, the variable coefficient a is updated based on a model training loss value by using the following formula:

a
  =
  
   a
   -
   
    a
    ⁢
    
     
      ∂
         
      Loss
     
     
      ∂
      t

for the input integrated-feature layer, taking a maximum value and an average value on a channel of each feature point;

Preferably, a formula for calculating the Sigmoid activation function is as follows:

Preferably, the method further includes:

Correspondingly, in another aspect of the present disclosure, a detection system of power equipment based on a multi-spectral image is further provided, including at least:

Preferably, the prediction processing unit further includes:

The attention adaptive processing unit further includes:

The embodiments of the present disclosure have the following beneficial effects:

The present disclosure provides a detection method and system of power equipment based on a multispectral image, which can quickly identify power equipment and types in a multi-spectral image (infrared, ultraviolet or visible image) by using a pixel-based power equipment detection algorithm and an image attention adaptive optimization method, and improve efficiency and accuracy of power equipment identification. A threshold brought by professionalism and experience can be lowered, and great convenience is provided for power equipment maintenance and operation staff.

In addition, adaptive learning of key information of the image is realized by using an attention mechanism method, so that redundancy of the model can be optimized, thereby improving application universality of the present disclosure.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Herein, it should also be noted that, to avoid obscuring the present disclosure due to unnecessary details, only the structure and/or processing steps closely related to the solutions of the present disclosure are shown in the accompanying drawings, and other details not greatly related to the present disclosure are omitted.

FIG. 1 is a schematic main flowchart of a detection method of power equipment based on a multispectral image according to an embodiment of the present disclosure. Referring to FIGS. 2 to 5 together, in this embodiment, the detection method of power equipment includes at least the following steps.

Step S10: Obtain an image of power equipment to be detected, where the image is one of an infrared image, an ultraviolet image, and a visible image.

In embodiments of the present disclosure, the obtained image only needs to be one of an infrared image, an ultraviolet image, and a visible image, and an image input format may be JPG or PNG format.

Step S11: Input the image into a pre-trained pixel-based power equipment detection model for detection, and perform classified prediction on pixels in the image to obtain a predicted result, where the power equipment detection model includes at least a trunk feature extraction unit, a feature integration processing unit, an attention adaptive processing unit, and a prediction and conversion unit.

In a specific example, as shown in FIG. 2, step S11 further includes the following steps.

Step S110: Convert (resize) the image into a predetermined uniform size, and extract a predetermined number of classes of preliminary effective features in the image by using the trunk feature extraction unit.

Step S111: Upsample the predetermined number of classes of preliminary effective features, and perform feature integration to obtain an integrated-feature layer.

Step S112: Process the integrated-feature layer by using the attention adaptive processing unit to obtain a processed adaptive integrated-feature layer.

Further, in an example, the attention adaptive processing unit further includes a channel attention processing unit, a spatial attention processing unit, and a weighting processing unit, and step S112 further includes the following steps.

Step S1120: Input 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 weight the integrated-feature layer by using the channel attention weight to obtain a channel integrated-feature layer.

Step S1121: Input 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 weight the integrated-feature layer by using the spatial attention weight to obtain a spatial integrated-feature layer.

It can be understood that in this embodiment, a formula for calculating the Sigmoid activation function involved in steps S1120 and S1121 is as follows:

Step S1122: Weight 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

Preferably, the variable coefficient a is updated based on a model training loss value by using the following formula:

a
  =
  
   a
   -
   
    a
    ⁢
    
     
      ∂
         
      Loss
     
     
      ∂
      t

Step S113: Predict the processed adaptive integrated-feature layer to obtain a result of classified prediction of the pixels in the image.

Step S114: Convert, based on the result of classified prediction of the pixels, gray levels of background pixels of the pixels into a predetermined value (to filter out the background).

Step S12: Output a predicted image based on the predicted result, where the predicted image is power equipment image with background information removed, and is marked with a name of each piece of equipment. A specific effect may be shown in FIG. 5, where the image on the left is the image of the input model and the image on the right is the predicted image.

It can be understood that in the present disclosure, the method needs to further include:

It can be understood that the power equipment detection model can predict image pixels by means of a codec structure. In the trained pixel-based power equipment detection model, the trunk feature extraction unit is configured to obtain one feature layer after another, and extract five preliminary effective features under stack of convolution and max pooling; the feature integration processing unit is configured to upsample the five preliminary effective features and perform feature integration to obtain an integrated-feature layer; the attention adaptive processing unit is configured to process the integrated-feature layer to obtain a processed adaptive integrated-feature layer; and the prediction and conversion unit is configured to predict the processed adaptive integrated-feature layer to obtain a result of classified prediction of the pixels in the image, and convert, based on the result of classified prediction of the pixels, gray levels of background pixels of the pixels into a predetermined value (that is, filter out the background).

FIG. 6 is a schematic structural diagram of a detection system of power equipment based on a multispectral image according to an embodiment of the present disclosure. Referring to FIGS. 7 and 8 together, the detection system 1 of power equipment includes at least:

More specifically, as shown in FIG. 7, the prediction processing unit 11 further includes:

More specifically, as shown in FIG. 8, the attention adaptive processing unit 112 further includes:

The variable coefficient a is updated based on a model training loss value by using the following formula:

a
  =
  
   a
   -
   
    a
    ⁢
    
     
      ∂
         
      Loss
     
     
      ∂
      t

For more details, reference may be made to the aforementioned descriptions of FIGS. 1 to 5, which will not be repeated herein.

The embodiments of the present disclosure have the following beneficial effects:

The present disclosure provides a detection method and system of power equipment based on a multispectral image, which can quickly identify power equipment and types in a multi-spectral image (infrared, ultraviolet or visible image) by using a pixel-based power equipment detection algorithm and an image attention adaptive optimization method, and improve efficiency and accuracy of power equipment identification. A threshold brought by professionalism and experience can be lowered, and great convenience is provided for power equipment maintenance and operation staff.

In addition, adaptive learning of key information of the image is realized by using an attention mechanism method, so that redundancy of the model can be optimized, thereby improving application universality of the present disclosure.

The present disclosure is described with reference to the flowcharts and/or block diagrams of the method, the device (system), and the computer program product according to the embodiments of the present disclosure. It should be understood that each flow and/or block in the flowchart and/or block diagram and a combination of the flow and/or block in the flowchart and/or block diagram can be implemented by computer program instructions. These computer program instructions may be provided for a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, such that instructions executed by the processor of the computer or other programmable data processing devices produce an apparatus configured to implement a function specified in one or more flows of the flowchart and/or one or more blocks of the block diagram.

The above descriptions are merely preferred embodiments of the present disclosure, and are not intended to limit the scope of the claims of the present disclosure, and thus all other equivalent changes or modifications that are completed without departing from the spirit disclosed by the present disclosure should fall within the scope of the claims of the present disclosure.