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Photovoltaic module hot spot defect detection method based on multi-scale feature map inference network

A multi-scale feature, photovoltaic module technology, applied in the field of defect detection, can solve problems such as poor detector robustness, achieve strong robustness, avoid inefficiency and uncertainty, and improve recognition capabilities.

Pending Publication Date: 2022-04-05
HEBEI UNIV OF TECH
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Problems solved by technology

[0004] In order to solve the problem of poor robustness of hot spot defect detectors in infrared images of different heights of photovoltaic power plants

Method used

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  • Photovoltaic module hot spot defect detection method based on multi-scale feature map inference network
  • Photovoltaic module hot spot defect detection method based on multi-scale feature map inference network
  • Photovoltaic module hot spot defect detection method based on multi-scale feature map inference network

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Embodiment 1

[0068] This embodiment provides a photovoltaic module hot spot defect detection method based on a multi-scale feature map reasoning network, which is characterized in that the specific steps of the method are:

[0069] The first step: the acquisition of data sets

[0070] 1.1 Original image acquisition: An infrared camera is mounted on the UAV, and the photo of the front of the photovoltaic module of the UAV at different flight heights is taken by the infrared camera; in this example, four heights of 28 meters, 35 meters, 47 meters and 60 meters were taken infrared image.

[0071] 1.2 Data set production: normalize the size of the photos obtained in step 1.1. The size of the infrared image in this example is 640×512, and 1,500 defect images are selected for each height to obtain a hot spot defect image library with a quantity of 6,000;

[0072] 1.3 Manually use LabelImg to mark the defect area of ​​each image in the hot spot defect image database with the hot spot defect type...

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Abstract

The invention discloses a photovoltaic module hot spot defect detection method based on a multi-scale feature map reasoning network, and the method comprises the steps: adding a self-designed multi-scale feature map reasoning module suitable for the hot spot defect detection of a photovoltaic power station into a feature fusion part in an original model based on an improved YOLOv4 neural network model; and multi-scale feature fusion is guided and complex background features in defect position area suppression images are highlighted, so that the identification capability of the hot spot defects of the photovoltaic module is effectively improved. According to the detection method, the deep learning technology and the image processing technology are combined, the low efficiency and uncertainty of traditional manual feature extraction are avoided, meanwhile, the detection process has high robustness, the detection precision is obviously improved, and the detection speed is increased.

Description

technical field [0001] The invention relates to the field of defect detection, and relates to a visual detection method for hot spot defects of photovoltaic power plants, in particular to a method for detecting hot spot defects of photovoltaic modules based on a multi-scale feature map reasoning network. Background technique [0002] Photovoltaic power generation has the advantages of non-pollution, renewable, universal resources, flexible mobility, good storage, safety and reliability, etc., and photovoltaic power generation systems have been widely used. With the continuous expansion of the scale of photovoltaic power plants, the application places of photovoltaic power generation systems are widely distributed, including large-scale ground photovoltaic power stations, roofs of residential and commercial buildings, photovoltaic street lights, etc. In these places, it is inevitable that buildings, tree shade, chimneys, dust, clouds, etc. will block photovoltaic modules and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/08G06N3/04G06V10/80G06V10/82G06K9/62G06N5/04
Inventor 陈海永赵参参王楚涵
Owner HEBEI UNIV OF TECH
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