红外热图像探测热斑的过程中,会受到复杂环境以及采集过程引入的诸多因素干扰,导致常规图像处理方法无法准确检测出热斑。提出一种改进的热斑检测方法,该方法根据灰度直方图特征进行自适应节点配置的B样条拟合,从而实现阈值选取和热斑分割,不仅同样具有抑制噪声的特点,且提高了热斑检测的准确率。实验证明该方法能实现含有大量噪声的光伏组件红外热图像的热斑检测,提高热斑检测的效率和准确率。
Abstract
In the process of hot spot detection with infrared thermal images, the thermal images will be interfered by many factors introduced by complex environment and acquisition process, resulting in the inability of conventional image processing methods to accurately detect hot spots. An improved hot spot detection method is proposed in this paper, which fits the histogram using the B-spline of adaptive knots according to the grayscale histogram features, so as to realize threshold selection and hot spot segmentation. The method not only has the same characteristics of suppressing noise, but also improves the accuracy of hot spot detection. Experimental results show that this method can realize the hot spot detection of infrared thermal images of photovoltaic modules containing a lot of noise and improve the efficiency and accuracy of hot spot detection.
关键词
光伏组件 /
热斑 /
红外热图像 /
B样条 /
自适应节点
Key words
photovoltaic modules /
hot spot /
infrared thermal image /
B-spline /
adaptive knots
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基金
国家重点研发计划（2018YFB1500800）; 中央高校基本科研业务费专项资金（PA2022GDGP0032）
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