Improved K-means Clustering-based Defect Detection Method for Photovoltaic Panels
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摘要: 为了能够对光伏组件热斑部分准确地识别和提取,提出了一种基于HSV空间模型的改进K均值聚类图像处理方法。首先,将红外图像进行HSV空间转换和双边滤波处理,去除噪声并提高图像对比度;其次,使用高斯核函数估计实现图像灰度概率密度函数提取,并以此获取初始聚类中心;最后,利用先验知识对图像进行K均值聚类,提取和量化热斑缺陷。研究结果表明,该方法能够快速地检测定位热斑位置并统计出光伏板损坏程度,具有较高的精度以及较好的灵敏性和稳定性。Abstract: An image processing method based on the HSV space model with an improved K-means clustering algorithm is proposed to accurately identify and extract the hot spot part of photovoltaic modules. First, the infrared image is transformed into the HSV space and bilaterally filtered to remove noise and improve the image contrast. Second, the Gaussian kernel function is used to extract the image grayscale probability density function, and then the initial clustering center is obtained. Finally, K-means clustering is applied to the image using prior knowledge to extract and quantify the hot spot defects. The research results show that the method can quickly detect and locate the hotspot position and calculate the degree of damage to the photovoltaic panel, and has high accuracy, good sensitivity, and stability.
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Key words:
- infrared image /
- defect detection /
- hot spot /
- photovoltaic module /
- HSV space model /
- improved K-means clustering
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表 1 禅思H20T红外相机参数
Table 1. Zenmuse H20T infrared camera parameters
Resolution Wavelength range Measuring range Focal length 640×512 8-14 μm −40℃ to 150℃ 13.5 mm 表 2 K-means改进效果评估
Table 2. Evaluation table of K-means improvement effect
Algorithm IE DBI SC Improved K-means 3.4843 0.4709 0.8274 Traditional K-means 3.5804 0.4719 0.8248 表 3 不同算法指标结果
Table 3. Results of different algorithm indexes
Algorithm Accuracy/% Precision/% Recall/% F-measure/% Improved K-means 90.86 95.95 85.54 90.45 Traditional K-means 84.92 82.67 81.58 82.12 Literature [8] 85.79 88.41 78.21 82.99 -
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