应用于电力设备图像的数学形态学边缘检测

Edge Detection of Power Equipment Image Based on Mathematical Morphology

摘要: 目的：近年来，电厂以及电网的新型化与网络化逐步展开，远程监测系统在电力行业中广泛应用，数字图像处理在其中扮演着重要的角色，边缘信息的提取对于图像后期处理的效果尤其关键。因此，提高图像检测的精准程度，对于全面推行和深化电力设备的维护与检修意义重大：一方面可以提升企业生产运输的效率；另一方面维护了厂区与工作人员的安全。方法：本文通过分析电力设备图像的特点，充分发挥数学形态学的优势，提出了一种自适应权重的多尺度多方向新型算子。并且引入权重的概念，使边缘检测模式与对应图像之间达到最佳配准，进而提取到更全面更精细的图像边缘。结果：大量数值实验表明，该算法可以更好地提取到边缘细节，其边缘检测评价指标(品质因数F)提升到了0.9以上的超高数值，表明提取到的图像边缘非常完整，而且对噪声的抑制与消除也有明显的改善。结论：本文所提出的多尺度多方向的数学形态学边缘检测新思想，在图像的边缘检测方面具有明显的优势，为电力公司的图像监测提供了有益思路和算法支撑，具有很好的应用价值。

Abstract: Objective: In recent years, power plants and power grids are developing into new and networked systems. Remote monitoring systems are widely used in the power industry, in which digital image processing plays an important role, and edge information extraction is especially critical for the effect of image post-processing. Therefore, improving the accuracy of image detection is of great significance for comprehensively carrying out and deepening the maintenance and overhaul of electric equipment. On the one hand, it can improve the production and transportation efficiency of enterprises; on the other hand, it maintains the safety of the factory and staff. Method: In this paper, by analyzing the characteristics of power equipment images and giving full play to the advantages of mathematical morphology, a new type of multi-scale and multi-direction operator with adaptive weight is proposed. The concept of weight is introduced to achieve the best registration between the edge detection mode and the corresponding image, and then a more comprehensive and fine image edge is extracted. Result: A large number of numerical experiments show that the algorithm can better extract edge details, and its edge detection and evaluation index (quality factor F) is raised to the ultra-high value above 0.9, indicating that the extracted image edge is very complete, and the noise suppression and elimination are also significantly improved. Conclusion: The new idea of multi-scale and multi-direction mathematical morphology edge detection proposed in this paper has obvious advantages in image edge detection, which provides beneficial ideas and algorithm support for image monitoring in power companies and has good application value.