Infrared image power equipment real time detection method based on deep learning

A technology for electrical equipment, infrared images, applied in the fields of instruments, biological neural network models, character and pattern recognition, etc., can solve problems such as unsatisfactory results

Inactive Publication Date: 2018-01-09
ZHEJIANG UNIV
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  • Abstract
  • Description
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Problems solved by technology

Traditional computer vision-based infrared image power equipment detection technology is still using artificially designed features, not only need to adjust many model parameters for the application in specific scenarios, but also when the background of the infrared image is relatively complex, the traditional method Unable to provide satisfactory results

Method used

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  • Infrared image power equipment real time detection method based on deep learning
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  • Infrared image power equipment real time detection method based on deep learning

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Embodiment Construction

[0044] The present invention is further described below.

[0045] Embodiments of the present invention and its implementation process are:

[0046] (1) Collect an infrared image I of a known device-level tag, and the device-level tag is [c i ,x i ,y i ,θ i ,w i , h i ], where i represents the i-th device, c i Indicates the category of the i-th device, and C is the total number of device categories. x i ,y i ,θ i ,w i , h i Respectively represent the x coordinate, y coordinate, tilt angle, width and height of the i-th device.

[0047] (2) Construct a neural network for power equipment detection based on the YOLO target detection framework; mainly including multi-scale feature extraction (Multi-scale feature extraction) steps, multi-task learning (Multi-task learning) steps and non-maximum value suppression (Non -maximum suppression) step.

[0048] (2.1) Multi-scale processing is performed on the infrared image I to obtain a series of feature maps of different scal...

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Abstract

The invention discloses an infrared image power equipment real time detection method based on deep learning; the method comprises the following steps: collecting a plurality of infrared images containing known power equipment, marking a target box for each infrared image, wherein the target box refers to an image area that contains a single known power device, and each infrared image has a devicelevel label; inputting the infrared images and the corresponding device level labels into a power equipment detection nerve network, and using a SGD algorithm with momentums to train the power equipment detection nerve network; using the trained power equipment detection nerve network to process an unknown to-be-tested image, thus obtaining a power equipment position and type in the unknown to-be-tested image. Compared with a conventional infrared image power equipment detection method, the infrared image power equipment real time detection method can obtain better performances, and can realize a real time processing speed.

Description

technical field [0001] The invention relates to an image target detection method, in particular to a real-time detection method for infrared image power equipment based on deep learning. Background technique [0002] Power equipment is the basic unit of power grid operation. Effective and accurate detection and evaluation of power equipment status is the premise of power equipment condition maintenance and life cycle management. The operation provides strong technical support. [0003] In order to perform fault diagnosis on electric equipment, it is first necessary to detect and locate the electric equipment in the image. Traditional computer vision-based infrared image power equipment detection technology is still using artificially designed features, not only need to adjust many model parameters for the application in specific scenarios, but also when the background of the infrared image is relatively complex, the traditional method Unable to provide satisfactory results...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
Inventor 姚祺龚小谨林颖
Owner ZHEJIANG UNIV
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