An Improved Neuron Segmentation Model for Crack Detection – Image Segmentation Model

Hao, Ming; Chen, Lu; Wang, Guoqing; Wang, Wei

doi:10.1515/cait-2017-0021

Cited by 9 publications

(1 citation statement)

References 42 publications

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“…Essentially, this article is based on the knowledge and experience gained in previous projects on crack detection in threedimensional CT data sets [PBE * 12], [MEL * 14], [ETR * 17], [PBB * 13], [EGD * 11], [PBE * 14]. A large part of the literature known to the author covers 2D crack detection, which is often carried out during so-called on-site inspections of structures by evaluating images of the surface [HLWW17], [Zhe14], [GSMM12], [CC17], [MK11]. A good overview of crack detection algorithms can be found in [MP17] 3D crack detection in homogeneous materials such as in [FE17] is often performed with combinations of simple image processing algorithms, which also provides good results with such data.…”

Section: Related Workmentioning

confidence: 99%

Possibilities and Limitations of Automatic Feature Extraction shown by the Example of Crack Detection in 3D-CT Images of Concrete Specimen

Paetsch¹

2019

eJNDT

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To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too.

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