融合持续同调-CNN的灰度化光伏红外图像的识别和分类

孙海蓉; 唐振超; 张洪玮; 周黎辉

doi:doi:10.19912/j.0254-0096.tynxb.2024-0084

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融合持续同调-CNN的灰度化光伏红外图像的识别和分类

更新时间：2025-12-02

- 融合持续同调-CNN的灰度化光伏红外图像的识别和分类
- Vol. 46, Issue 6, Pages: 321-328(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0084
  CLC： TP391
- Published Online：07 July 2025，
  
  Published：2025
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孙海蓉, 唐振超, 张洪玮, et al. 融合持续同调-CNN的灰度化光伏红外图像的识别和分类[J]. 2025, 46(6): 321-328.
DOI：

孙海蓉, 唐振超, 张洪玮, et al. 融合持续同调-CNN的灰度化光伏红外图像的识别和分类[J]. 2025, 46(6): 321-328. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0084.

摘要

针对卷积神经网络对光伏红外热斑图像进行识别和分类准确率低、计算量大、光伏红外图像上热斑特征难以识别的问题

提出一种基于持续同调的对灰度化光伏热斑图像提取拓扑特征的算法。首先

将光伏红外热斑图像灰度化;然后将灰度化之后的图像进行持续同调计算

得到条形码

从条形码中提取其拓扑特征组成新的图像;最后

用卷积神经网络对新的图像进行识别和分类。实验结果表明

灰度化后的光伏红外图像是一个单通道图像

计算量更小;提取的光伏红外热斑图像拓扑特征更易识别和分类

准确率更高。

Abstract

Aiming at the low accuracy and high computational complexity of convolutional neural networks in recognizing and classifying photovoltaic infrared hot spot images

as well as the difficulty in identifying the hot spot features on photovoltaic infrared images

an algorithm based on persistent homology is proposed to extract topological features from grayscale photovoltaic hot spot images. Firstly

the photovoltaic infrared hot spot image is converted to grayscale. Then

persistent homology calculation is performed on the grayscale image to obtain a barcode

from which the topological features are extracted to compose a new image. Finally

a convolutional neural network is employed to recognize and classify the new image. The experimental results demonstrate that the grayscale photovoltaic infrared image is a single-channel image

resulting in lower computational complexity. The extracted topological features of the photovoltaic infrared hot spot image are easier to identify and classify

leading to higher accuracy.

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