The quantitative analysis of rock mass damage is crucial in fields such as engineering geology, disaster prevention, mining, geotechnical engineering, and structural engineering. With the advancement and application of noncontact measurement technologies and fractal theory, image-based damage identification methods are gaining increasing importance. This paper presents an optimized binarization algorithm for identifying and characterizing damage zones in granite explosion images. The method involves filtering, mathematical morphology operations, and connectivity recognition to effectively remove background noise while preserving clear boundaries of the damaged areas. It accurately captures the explosion damage in granite, both in terms of damage morphology and characteristic parameters. Additionally, the coefficient of agreement (COA) is introduced to quantitatively assess the accuracy of different methods in identifying damaged areas. The experimental results show that, compared with commonly used methods such as Otsu's method, Bernsen's algorithm, Niblack's algorithm, Sauvola's algorithm, and the K-means image clustering algorithm, the proposed method performs better in terms of identification accuracy and parameter agreement, achieving COA values near 1 across diverse experimental environments. Furthermore, the proposed method excels in handling uneven lighting, mitigating interference from rock surface textures and explosion carbonization zones, and demonstrates significant robustness in complex scenarios. The findings of this paper provide insights into the integration of engineering geology and computer vision technology. They offer valuable references for damage identification in excavation damage zones (EDZs), geological disaster evaluation, and structural damage warning systems.

中文翻译：

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基于优化二值化算法的岩体爆炸损伤图像识别和表征方法


岩体损伤的定量分析在工程地质、防灾、采矿、岩土工程和结构工程等领域至关重要。随着非接触式测量技术和分形理论的进步和应用，基于图像的损伤识别方法变得越来越重要。本文提出了一种优化的二值化算法，用于识别和表征花岗岩爆炸图像中的损伤区。该方法涉及滤波、数学形态学运算和连接识别，以有效去除背景噪声，同时保持受损区域的清晰边界。它准确地捕捉了花岗岩中的爆炸损伤，包括损伤形态和特征参数。此外，还引入了一致性系数 （COA） 来定量评估不同方法识别受损区域的准确性。实验结果表明，与常用的Otsu方法、Bernsen算法、Niblack算法、Sauvola算法和K-means图像聚类算法相比，所提方法在识别精度和参数一致性方面表现更好，在不同实验环境中实现了接近1的COA值。此外，所提出的方法在处理不均匀的照明、减轻岩石表面纹理和爆炸碳化区的干扰方面表现出色，并在复杂场景中表现出显着的鲁棒性。本文的研究结果为工程地质学和计算机视觉技术的整合提供了见解。 它们为开挖损伤区 （EDZ） 的损伤识别、地质灾害评估和结构损伤预警系统提供了有价值的参考。