基于图像处理算法的金属结构腐蚀强度研究

Virupaxayya V. Hosallimath, A. Awati, M. Patil
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引用次数: 1

摘要

利用腐蚀检测算法对金属结构的腐蚀强度进行检测是一种先进的腐蚀检测方法,因为传统的方法在面积大的地方耗时长,在交通不便的地方无法进行检测。人类对腐蚀强度的估计可能不太准确,对金属表面的错误分析可能导致材料的破坏或损失。针对上述需求,开发了智能腐蚀检测算法,通过一系列摄影图像自动检测和检测金属表面的腐蚀。黑色像素百分比是分析腐蚀强度的参数之一。第一种算法首先将RGB图像转换为灰度图像,并使用Otsu方法进行二值化,从二值化图像中计算PBP。PBP值越高,腐蚀强度越高。另一种研究腐蚀的方法是通过验证金属表面的粗糙度和光滑度,因为粗糙的表面表明存在腐蚀。这是通过计算图像的能量来实现的,这是GLCM的特性之一。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigation of Corrosion Intensity on Metal Structures Using Image Processing Algorithms
An investigation of corrosion intensity on metal structures using corrosion detection algorithms is one of the advanced methods for corrosion detection since the traditional method is time-consuming for vast areas and impossible to investigate in inaccessible areas. Estimation of the intensity of the corrosion by humans may not have much accuracy, and false analysis of the metal surfaces may lead to the destruction or loss of the material. To cater to the above needs intelligent corrosion detection algorithms are developed, which automatically detect and inspect the corrosion on metal surfaces by using a series of photographic images. percentage of black pixels is one of the parameters for analyzing the corrosion intensity. The first algorithm initially converts the RGB image to a grayscale image and it is binarized using Otsu's method, and from the binary image, PBP is calculated. The higher the value of PBP higher is the corrosion intensity. Another approach made to investigate the corrosion is by verifying the roughness and smoothness of the metal surface since a rough surface indicates the presence of corrosion. This is achieved by calculating the energy of the image, which is one of the properties of GLCM.
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