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Lightweight target detection and fault identification method, device and system

A target detection and fault identification technology, applied in neural learning methods, character and pattern recognition, instruments, etc., can solve the problems of difficult and lightweight equipment application, complex convolutional neural network network structure, and large number of parameters. Engineering application value, guaranteed performance, and the effect of improving detection performance

Pending Publication Date: 2021-10-29
STATE GRID ELECTRIC POWER RES INST +4
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The convolutional neural network has a complex network structure and a large number of parameters, and the model size generally reaches tens of megabytes or even hundreds of megabytes, which is difficult to apply on lightweight devices

Method used

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  • Lightweight target detection and fault identification method, device and system
  • Lightweight target detection and fault identification method, device and system
  • Lightweight target detection and fault identification method, device and system

Examples

Experimental program
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Embodiment 1

[0052]The embodiment of the present invention provides a light-weight target detection and fault identification method, which can be oriented to the power inspection scene of the smart grid, and includes the following steps:

[0053] Step (1) Obtain the original lightweight target detection and fault identification network model;

[0054] Step (2) optimizing the parameters of the original lightweight object detection and fault identification network model to obtain an optimized lightweight object detection and fault identification network model;

[0055] Step (3) using the optimized lightweight target detection and fault identification network model to perform target detection and fault identification on the received power inspection image data.

[0056] In a specific implementation of the embodiments of the present invention, the following steps are also included before the step (1):

[0057] Obtain the power inspection scene image as the original data set. The power inspect...

Embodiment 2

[0102] Based on the same inventive concept as in Embodiment 1, the embodiment of the present invention provides a light-weight target detection and fault identification device, including:

[0103] An acquisition module, configured to acquire an original lightweight target detection and fault identification network model;

[0104] An optimization module, configured to optimize the parameters of the original lightweight object detection and fault identification network model to obtain an optimized lightweight object detection and fault identification network model;

[0105] The detection and recognition module is used to perform target detection and fault recognition on the received power inspection image data by using the optimized lightweight target detection and fault recognition network model.

[0106] All the other parts are the same as in Example 1.

Embodiment 3

[0108] An embodiment of the present invention provides a lightweight target detection and fault identification system, including a storage medium and a processor;

[0109] The storage medium is used to store instructions;

[0110] The processor is configured to operate according to the instructions to execute the method according to any one of Embodiment 1.

[0111] Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

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Abstract

The invention discloses a lightweight target detection and fault identification method, device and system. The method comprises the following steps: acquiring an original lightweight target detection and fault identification network model; performing parameter optimization on the original lightweight target detection and fault identification network model to obtain an optimized lightweight target detection and fault identification network model; and performing target detection and fault identification on the received power inspection image data by using the optimized lightweight target detection and fault identification network model. According to the invention, the lightweight design of the network is realized so as to obtain a good compromise between the detection rate and the detection precision.

Description

technical field [0001] The invention belongs to the technical field of image recognition and fault detection applied to electric power patrol inspection, and in particular relates to a light-weight target detection and fault recognition method, device and system. Background technique [0002] With the development of the national economy and the continuous improvement of people's living standards, the scale of the power grid continues to expand. The reliable operation of power grid lines and power grid equipment directly affects the production safety and social benefits of power companies. UAV power inspection operations gradually replace manual inspection operations, greatly improving inspection efficiency and saving labor costs. [0003] The traditional UAV inspection target detection and fault identification method is generally divided into two steps. One is based on the manual pre-defined feature extraction method. After abstracting and analyzing the original data, the ma...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/32G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/253G06F18/214
Inventor 李洋蒋元晨郝悍勇龚亮亮胡阳周子纯袁逸凡丁忠林吕超张影汤亿则冯宝张铖
Owner STATE GRID ELECTRIC POWER RES INST