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Research on power equipment recognition method based on image processing

机译:基于图像处理的电力设备识别方法研究

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Abstract Electric energy is an indispensable energy in life, and the power network is the basis to ensure its normal circulation, in which the operation status of power equipment is one of the key factors to determine the safe and stable operation of the power network. In the information age, the traditional manual periodic inspection and the existing method of relying on manual monitoring equipment operation status can no longer meet the needs of safe operation of equipment; relying on computer technology and image recognition technology to achieve automatic identification of power equipment has become a research hot spot. In order to realize automatic identification of power equipment, this paper presents a method of recognition of power equipment based on image processing. Firstly, the power equipment image is preprocessed by various denoising and sharpening algorithms to remove the noise and distortion of the image and improve the image quality; secondly, the SIFT algorithm is used to extract image features, and PCA algorithm is used to reduce the dimension; finally, the support vector machine is used to classify and recognize the image. The simulation results show that the proposed denoising and sharpening algorithms can process images well and improve the quality of images. The support vector machine is used to classify the image features processed by SIFT algorithm and PCA algorithm, and the automatic recognition of power equipment is realized. And the method of power identification based on image processing proposed in this paper has good recognition accuracy.
机译:摘要电能是生活中不可或缺的能量,电网是确保其正常循环的基础,其中电力设备的操作状态是确定电网安全稳定运行的关键因素之一。在信息时代,传统的手动定期检查和现有的依托手动监控设备运行状态的方法可以不再满足设备安全运行的需求;依靠计算机技术和图像识别技术实现电力设备的自动识别已成为研究热点。为了实现电力设备的自动识别,本文提出了一种基于图像处理的电力设备识别方法。首先,通过各种去噪和锐化算法预处理电力设备图像以消除图像的噪声和失真,提高图像质量;其次,SIFT算法用于提取图像特征,使用PCA算法来减小维度;最后,支持向量机用于对图像进行分类和识别。仿真结果表明,所提出的去噪和锐化算法可以处理良好的图像并提高图像的质量。支持向量机用于对SIFT算法和PCA算法处理的图像特征进行分类,并且实现了电力设备的自动识别。基于本文提出的图像处理的功率识别方法具有良好的识别精度。

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