Analysis of SEM digital images to quantify crack network pattern area in chromium electrodeposits

Vidal, Maider; Ostra, Miren; Imaz, N.; García-Lecina, Eva; Ubide, Carlos

doi:10.1016/j.surfcoat.2015.11.049

Cited by 35 publications

(15 citation statements)

References 26 publications

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“…There is a considerable amount of research effort devoted towards concrete crack detection for the surface-level SHM of civil infrastructures in general and bridges in particular [ 37 , 48 , 51 , 88 , 163 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 ]. Due to the concrete-based composition of the majority of civil infrastructures (e.g., dams, roads, buildings, sewers, bridges, tunnels), the techniques developed for concrete crack detection within a particular type of concrete structure can also be generalized towards other civil infrastructures.…”

Section: Algorithms For Data Analysismentioning

confidence: 99%

“…Some of the earlier works focused on the utilization of basic-level image-processing techniques for crack detection in concrete structures [ 48 , 51 , 88 ], which included basic-level morphological approaches [ 166 , 190 , 191 , 192 , 193 ], digital image correlation techniques [ 194 , 195 , 196 , 197 ] and different segmentation-based approaches [ 198 ]. A number of different image-based filtering techniques were also employed, namely Gabor filtering [ 199 ], median filtering [ 184 , 200 ], texture filtering [ 201 , 202 ] and data fusion-based filtering approaches [ 176 , 203 ]. The efficacy of different image transformation techniques has also been discussed, ranging from the watershed transform [ 190 , 192 , 194 ], wavelet transform [ 200 , 201 , 204 , 205 ], and randomized Hough transform [ 200 , 206 ].…”

Section: Algorithms For Data Analysismentioning

confidence: 99%

“…A number of different image-based filtering techniques were also employed, namely Gabor filtering [ 199 ], median filtering [ 184 , 200 ], texture filtering [ 201 , 202 ] and data fusion-based filtering approaches [ 176 , 203 ]. The efficacy of different image transformation techniques has also been discussed, ranging from the watershed transform [ 190 , 192 , 194 ], wavelet transform [ 200 , 201 , 204 , 205 ], and randomized Hough transform [ 200 , 206 ]. The use of fast Fourier and fast Haar algorithms with Sobel and Canny edge detectors was developed for the concrete crack detection in one of the earlier studies [ 207 ].…”

Section: Algorithms For Data Analysismentioning

confidence: 99%

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Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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The non-destructive evaluation (NDE) of civil infrastructure has been an active area of research in recent decades. The traditional inspection of civil infrastructure mostly relies on visual inspection using human inspectors. To facilitate this process, different sensors for data collection and techniques for data analyses have been used to effectively carry out this task in an automated fashion. This review-based study will examine some of the recent developments in the field of autonomous robotic platforms for NDE and the structural health monitoring (SHM) of bridges. Some of the salient features of this review-based study will be discussed in the light of the existing surveys and reviews that have been published in the recent past, which will enable the clarification regarding the novelty of the present review-based study. The review methodology will be discussed in sufficient depth, which will provide insights regarding some of the primary aspects of the review methodology followed by this review-based study. In order to provide an in-depth examination of the state-of-the-art, the current research will examine the three major research streams. The first stream relates to technological robotic platforms developed for NDE of bridges. The second stream of literature examines myriad sensors used for the development of robotic platforms for the NDE of bridges. The third stream of literature highlights different algorithms for the surface- and sub-surface-level analysis of bridges that have been developed by studies in the past. A number of challenges towards the development of robotic platforms have also been discussed.

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Section: Algorithms For Data Analysismentioning

confidence: 99%

Section: Algorithms For Data Analysismentioning

confidence: 99%

Section: Algorithms For Data Analysismentioning

confidence: 99%

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Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Ahmed

Gucunski

2020

Sensors

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“…The method was found effective in analysing the content of GBFS in hardened cement pastes. Concurrently, [34] investigated the image processing technique in quantifying cracks area in electrodeposits. The study uses the second derivative of the histogram obtained with Laplacian of Gaussian (LoG) together with Prewitt vertical edge detector to generally access the spatial cracked area by comparing the data with the subjective visual assessment.…”

Section: Microstructure Properties Evaluationmentioning

confidence: 99%

Digital Analysis of Geo-Referenced Concrete Scanning Electron Microscope (SEM) Images

Ahamad

Maizul

2020

Civil and Environmental Engineering Reports

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The microstructural evaluation of complex cementitious materials has been made possible by the microscopic imaging tools such as Scanning Electron Microscope (SEM) and X-Ray Microanalysis. Particularly, the application of concrete SEM imaging and digital image analysis have become common in the analysis and mapping of concrete technology. In this study, six samples of two-dimensional (2D) SEM images were spatially resampled to produce Geo-referenced SEM sample images. Subsequently, they were analyzed and the intensity histogram plot was produced to facilitate visual interpretation. The consecutive digital image analysis performed was the enhancement and noise removal process using two filtering methods i.e. median and adaptive box filter. The filtered resampled images, then undergone the unsupervised K-Means classification process to collectively separate each individual pixel corresponds to the spectral data. By spatial segmentation of K-Means algorithms, the cluster groups generated were carefully reviewed before proceeding to the final analysis. From the resulting data, the mapping of the spatial distribution of k-cluster and the quantification of micro-cracks (voids) were performed. The results of the SEM images (1st - 4th sample) showed a higher percentage of k-cluster data indicating a good correlation with the major elemental composition of EDX analysis, namely Oxide (O), Silicon (Si) and Carbon (C). Meanwhile, the subjective visual assessment of the image (5th and 6th sample) has confirmed the micro-crack developments on the concrete SEM images upon which the crack density was 3.02 % and 1.30 %, respectively.

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“…The methods for detecting defects are classified into four main categories: statistical [1], structural, filter-based [2], and model-based. The techniques based on the analysis of histograms [3,4], co-occurrence matrices [5], and local binary patterns [6,7] are also used. The approaches based on the use of neural networks are used increasingly more often.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks

et al. 2018

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The research of fractographic images of metals is an important method that allows obtaining valuable information about the physical and mechanical properties of a metallic specimen, determining the causes of its fracture, and developing models for optimizing its properties. One of the main lines of research in this case is studying the characteristics of the dimples of viscous detachment, which are formed on the metal surface in the process of its fracture. This paper proposes a method for detecting dimples of viscous detachment on a fractographic image, which is based on using a convolutional neural network. Compared to classical image processing algorithms, the use of the neural network significantly reduces the number of parameters to be adjusted manually. In addition, when being trained, the neural network can reveal a lot more characteristic features that affect the quality of recognition in a positive way. This makes the method more versatile and accurate. We investigated 17 models of convolutional neural networks with different structures and selected the optimal variant in terms of accuracy and speed. The proposed neural network classifies image pixels into two categories: “dimple” and “edge”. A transition from a probabilistic result at the output of the neural network to an unambiguously clear classification is proposed. The results obtained using the neural network were compared to the results obtained using a previously developed algorithm based on a set of filters. It has been found that the results are very similar (more than 90% similarity), but the neural network reveals the necessary features more accurately than the previous method.

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Analysis of SEM digital images to quantify crack network pattern area in chromium electrodeposits

Cited by 35 publications

References 26 publications

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Review of Non-Destructive Civil Infrastructure Evaluation for Bridges: State-of-the-Art Robotic Platforms, Sensors and Algorithms

Digital Analysis of Geo-Referenced Concrete Scanning Electron Microscope (SEM) Images

Investigation of the Rupture Surface of the Titanium Alloy Using Convolutional Neural Networks

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