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Curve-based crack detection using crack information gain
Structural Control and Health Monitoring ( IF 4.6 ) Pub Date : 2021-05-06 , DOI: 10.1002/stc.2764
Qi Chen 1 , Yuchun Huang 1 , Xingxing Weng 1 , Wenjun Liu 1
Affiliation  

Crack detection provides valuable information concerning the location, extent, type, and severity for structural health monitoring of civil infrastructures. Due to the influence of uneven lighting and imaging noise, as well as the debris, various textures, and materials of the different civil structure, detecting cracks in the surface images of civil infrastructures remains challenging in that there are many false positives and defragmentation of the crack curve. In this paper, an information-based crack detection method is developed to robustly characterize and detect cracks on a curve-by-curve basis. Crack information gain (CIG) is defined to characterize the information of a local patch's being cracked. With the proposed model of estimating crack depth, the information-based crack descriptor CIG is calculated by statistically modeling the probabilistic distribution of crack depth. Secondly, crack curves of different saliency are progressively detected by iteratively searching the salient string of pixels, regardless of the small gaps between fragments of the crack curve. Finally, crack curves are validated by examining the variation of CIG along the crack curve. The experimental results on a diverse set of images of different civil infrastructures demonstrate the generalizability of the proposed method, and the overall performance on each dataset outperforms the state-of-the-art available methods with 2.9% improvement. The proposed method has potential for the quantitative evaluation of cracks on a meaningful curve basis.

中文翻译:

使用裂纹信息增益的基于曲线的裂纹检测

裂缝检测为民用基础设施的结构健康监测提供有关位置、范围、类型和严重性的宝贵信息。由于光照不均和成像噪声的影响,以及不同土木结构的碎片、各种纹理和材料的影响,检测民用基础设施表面图像的裂缝仍然具有挑战性,因为存在许多误报和碎片整理裂纹曲线。在本文中,开发了一种基于信息的裂纹检测方法,以在逐条曲线的基础上稳健地表征和检测裂纹。裂纹信息增益 (CIG) 被定义为表征被破解的局部补丁的信息。利用所提出的估计裂纹深度的模型,基于信息的裂纹描述符 CIG 是通过对裂纹深度的概率分布进行统计建模来计算的。其次,通过迭代搜索显着像素串来逐步检测不同显着性的裂纹曲线,而不管裂纹曲线片段之间的小间隙。最后,通过检查 CIG 沿裂纹曲线的变化来验证裂纹曲线。在不同民用基础设施的不同图像集上的实验结果证明了所提出方法的普遍性,每个数据集的整体性能优于最先进的可用方法,提高了 2.9%。所提出的方法具有在有意义的曲线基础上定量评估裂纹的潜力。通过迭代搜索显着像素串来逐步检测不同显着性的裂纹曲线,而不管裂纹曲线片段之间的小间隙。最后,通过检查 CIG 沿裂纹曲线的变化来验证裂纹曲线。在不同民用基础设施的不同图像集上的实验结果证明了所提出方法的普遍性,每个数据集的整体性能优于最先进的可用方法,提高了 2.9%。所提出的方法具有在有意义的曲线基础上定量评估裂纹的潜力。通过迭代搜索显着像素串来逐步检测不同显着性的裂纹曲线,而不管裂纹曲线片段之间的小间隙。最后,通过检查 CIG 沿裂纹曲线的变化来验证裂纹曲线。在不同民用基础设施的不同图像集上的实验结果证明了所提出方法的普遍性,每个数据集的整体性能优于最先进的可用方法,提高了 2.9%。所提出的方法具有在有意义的曲线基础上定量评估裂纹的潜力。通过检查 CIG 沿裂纹曲线的变化来验证裂纹曲线。在不同民用基础设施的不同图像集上的实验结果证明了所提出方法的普遍性,每个数据集的整体性能优于最先进的可用方法,提高了 2.9%。所提出的方法具有在有意义的曲线基础上定量评估裂纹的潜力。通过检查 CIG 沿裂纹曲线的变化来验证裂纹曲线。在不同民用基础设施的不同图像集上的实验结果证明了所提出方法的普遍性,每个数据集的整体性能优于最先进的可用方法,提高了 2.9%。所提出的方法具有在有意义的曲线基础上定量评估裂纹的潜力。
更新日期:2021-07-05
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