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.
关键词
特征提取 /
卷积神经网络 /
持续同调 /
拓扑数据分析 /
拓扑特征 /
识别和分类
Key words
feature extraction /
convolutional neural network /
persistent homology /
topological data analysis /
topological feature /
identification and classification
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基金
河北省省级科技计划资助（22567643H）
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