摘要
在光伏红外热图像中,热斑和部分高温工作区的亮度非常接近。在利用传统的阈值分割技术提取热斑时,往往会将工作区也一并分割出来,形成虚假热斑。结合Otsu算法和Sauvola算法的优点,提出了一种基于加权灰度图的混合阈值分割方法。通过对灰度图加权处理,降低工作区亮度,从而增强热斑与工作区的对比度,改善热斑的可视性。利用Otsu算法与Sauvola算法二值化灰度图,并根据二值图差异度计算混合阈值,以此消除虚假热斑的干扰。实验证明,该方法适用于检测存在高温工作区的光伏板热斑,能够精准有效地分割热斑。
In the photovoltaic infrared thermal image,the brightness of the hot spot and part of the high-temperature working area is very close.When the traditional threshold segmentation technique is used to extract hot spots,the working area is often separated to form false hot spots.Combining the advantages of Otsu algorithm and Sauvola algorithm,a hybrid threshold segmentation method based on weighted gray image is proposed.The brightness of the working area is reduced by weighting the gray-scale image,so that the contrast between the hot spot and the working area is enhanced and the visibility of the hot spot is improved.By using Otsu algorithm and the Sauvola algorithm,the gray-level image is binarized,and the mixing threshold is calculated according to the difference degree of the binary image.The experiments results show that this method is suitable for detecting hot spots of photovoltaic panels in hot working area and can segment hot spot accurately and effectively.
作者
孙海蓉
伍金文
SUN Hairong;WU Jinwen(Department of Automation,North China Electric Power University,Baoding 071003,China)
出处
《电力科学与工程》
2024年第1期63-68,共6页
Electric Power Science and Engineering
基金
河北省省级科技计划资助项目(22567643H)。
