Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

Arbaoui, Ahcene; Ouahabi, Abdeldjalil; Jacques, Sébastien; Hamiane, Madina

doi:10.3221/igf-esis.58.03

Cited by 19 publications

(14 citation statements)

References 56 publications

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“…Our future contribution is a technological breakthrough that includes introducing a layer of intelligence at the acquisition level aimed at automatically determining image texture and its quality in terms of noise level, blur and shooting conditions (lighting, inpainting, registration, occlusion, low resolution, etc.) [ 56 , 57 , 58 , 59 , 60 , 61 ] in order to automatically adjust the parameters necessary for an optimal use of the proposed DCSR algorithm.…”

Section: Discussionmentioning

confidence: 99%

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Mahdaoui

Ouahabi

Moulay

2022

Sensors

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In remote sensing applications and medical imaging, one of the key points is the acquisition, real-time preprocessing and storage of information. Due to the large amount of information present in the form of images or videos, compression of these data is necessary. Compressed sensing is an efficient technique to meet this challenge. It consists in acquiring a signal, assuming that it can have a sparse representation, by using a minimum number of nonadaptive linear measurements. After this compressed sensing process, a reconstruction of the original signal must be performed at the receiver. Reconstruction techniques are often unable to preserve the texture of the image and tend to smooth out its details. To overcome this problem, we propose, in this work, a compressed sensing reconstruction method that combines the total variation regularization and the non-local self-similarity constraint. The optimization of this method is performed by using an augmented Lagrangian that avoids the difficult problem of nonlinearity and nondifferentiability of the regularization terms. The proposed algorithm, called denoising-compressed sensing by regularization (DCSR) terms, will not only perform image reconstruction but also denoising. To evaluate the performance of the proposed algorithm, we compare its performance with state-of-the-art methods, such as Nesterov’s algorithm, group-based sparse representation and wavelet-based methods, in terms of denoising and preservation of edges, texture and image details, as well as from the point of view of computational complexity. Our approach permits a gain up to 25% in terms of denoising efficiency and visual quality using two metrics: peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

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Section: Discussionmentioning

confidence: 99%

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Mahdaoui

Ouahabi

Moulay

2022

Sensors

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“…The value at which the structure completely loses its ability to resist the load and its destruction occurs are taken as the value of the maximum bearing capacity of the support. Multiple cracking of concrete requires the development of non-destructive methods of structural control in difficult operating conditions [81,82].…”

Section: Discussionmentioning

confidence: 99%

Strain Hardening of Polypropylene Microfiber Reinforced Composite Based on Alkali-Activated Slag Matrix

Smirnova

Pidal²,

Alekseev³

et al. 2022

Materials

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A comparative study of the fracture features, strength and deformation properties of pseudo strain-hardening composites based on alkali-activated slag and Portland cement matrices with polypropylene microfiber was carried out. Correlations between their compositions and characteristics of stress–strain diagrams under tension in bending with an additional determination of acoustic emission parameters were determined. An average strength alkali-activated slag matrix with compressive strength of 40 MPa and a high-strength Portland cement matrix with compressive strength of 70 MPa were used. The matrix compositions were selected for high filling the composites with polypropylene microfiber in the amount of 5%-vol. and 3.5%-vol. ensuring the workability at the low water-to-binder ratios of 0.22 and 0.3 for Portland cement and alkali-activated slag matrices, respectively. Deformation diagrams were obtained for all studied compositions. Peaks in the number of acoustic signals in alkali-activated slag composites were observed only in the strain-softening zone. Graphs of dependence of the rate of acoustic events occurrence in samples from the start of the test experimentally prove that this method of non-destructive testing can be used to monitor structures based on strain-hardening composites.

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“…− Using generative adversarial networks [62−65] to preprocess, colorize, correct, and enhance images before presenting them to the segmentation algorithm. − Combining the features extracted by the proposed approach with deeplearned features [66][67][68][69][70][71][72]7] to improve the quality of the segmentation and make it more semantic.…”

Section: Discussionmentioning

confidence: 99%

Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

Atia¹,

Benzaoui²,

Jacques³

et al. 2022

Preprint

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Segmentation of brain tumor images is a major research topic in medical imaging to have a refined detection and understanding of abnormal masses in the brain. This paper proposes a new segmentation method, consisting of three main steps, to detect brain lesions using magnetic resonance imaging (MRI). In the first step, the parts of the image delineating the skull bone are removed to exclude insignificant data. In the second step, which is the main contribution of this study, the particle swarm optimization (PSO) technique is applied to detect the block that contains the brain lesions. The fitness function, used to determine the best block among all candidate blocks, is based on a two-way fixed-effects analysis of variance (ANOVA). In the last step of the algorithm, the K-means segmentation method is used in the lesion block to classify it as tumor or not. A thorough evaluation of the proposed algorithm is performed using the MRI database provided by the Kouba imaging center in Algiers, Algeria. Estimates of the selected fitness function are first compared to those based on the sum-of-absolute-differences (SAD) dissimilarity criterion and demonstrate the efficiency and robustness of the ANOVA. The performance of the optimized brain tumor segmentation algorithm is then compared to the results of several state-of-the-art techniques, including fuzzy C-means, K-means, Otsu thresholding, local thresholding, and watershed segmentation. The results obtained using Dice coefficient, Jaccard distance, correlation coefficient, and root mean square error (RMSE) measurements demonstrate the superiority of the proposed optimized segmentation algorithm over equivalent techniques.

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Wavelet-based multiresolution analysis coupled with deep learning to efficiently monitor cracks in concrete

Cited by 19 publications

References 56 publications

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Image Denoising Using a Compressive Sensing Approach Based on Regularization Constraints

Strain Hardening of Polypropylene Microfiber Reinforced Composite Based on Alkali-Activated Slag Matrix

Particle Swarm Optimization and Two-Way Fixed-Effects Analysis of Variance for Efficient Brain Tumor Segmentation

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