Power equipment identification method and system, medium and electronic equipment

Abstract

The invention provides a power equipment identification method and system, a medium and electronic equipment. The method comprises the following steps: acquiring a to-be-identified image; according to the obtained image and a preset convolutional neural network model, obtaining a positioning identification result of the electric power external insulation equipment, wherein the preset convolutional neural network model adopts a YOLO-V3 model, a standard convolutional structure in a basic network Darknet-53 of the YOLO-V3 model is replaced by a deep separable convolutional structure, and a full connection layer and a Softmax layer of the Darknet-53 are removed; according to the method and the device, the detection capability of the model on a small target is enhanced while the real-time performance is high, and real-time and efficient target detection can be better realized on embedded terminal equipment.

Description

technical field [0001] The present disclosure relates to the technical field of electric equipment identification, and in particular to an electric equipment identification method, system, medium and electronic equipment. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] In the power scene, power equipment is easily affected by illumination changes, angle changes, partial occlusion, deformation, blur and background interference, and the structure of power equipment is complex, there are many types, and the similarity of the same type of equipment is high. The complex environment creates great difficulties for recognition, making target recognition extremely challenging. [0004] The inventors found that most of the existing methods use deep learning methods, such as power equipment recognition based on convolutional neural network models,...

AI Technical Summary

Problems solved by technology

[0003] In the power scene, power equipment is easily affected by illumination changes, angle changes, partial occlusion, deformation, blur and background interference, and the structure of power equipment is complex, there are many types, and the similarity of the same type of equipment is high. The complex environment makes recognition more difficult, making target recognition extremely challenging

[0004] The inventors found that most of the existing methods use deep learning methods, such as power equipment recognition based on convolutional neural network models, power equipment recognition based on long-term and short-term memory networks, etc., but most of the existing deep learning methods are structured Complexity, a large amount of parameters, and a large amount of resources are occupied, which makes it difficult to meet the real-time requirements of power equipment identification

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