Concrete Multi-Type Defect Classification Algorithm Based on MSSMA-SVM

Xu, Tongwen; Ai, Jun; Ma, Zizhu; Jian, Bijian; Ma, Chunbo

doi:10.3390/s22239145

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…The typical defect detection algorithms in concrete defect detection are the machine learning algorithm [27][28][29], the synthetic aperture focusing algorithm [30,31], and the modal analysis algorithm [32][33][34], among others. Machine learning-based defect detection algorithms generally have high defect recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Internal Defect Detection Algorithm for Concrete Blocks Based on Local Mean Decomposition-Singular Value Decomposition and Weighted Spatial-Spectral Entropy

et al. 2023

Entropy

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Within the scope of concrete internal defect detection via laser Doppler vibrometry (LDV), the acquired signals frequently suffer from low signal-to-noise ratios (SNR) due to the heterogeneity of the concrete’s material properties and its rough surface structure. Consequently, these factors make the defect signal characteristics challenging to discern precisely. In response to this challenge, we propose an internal defect detection algorithm that incorporates local mean decomposition-singular value decomposition (LMD-SVD) and weighted spatial-spectral entropy (WSSE). Initially, the LDV vibration signal undergoes denoising via LMD and the SVD algorithms to reduce noise interference. Subsequently, the distribution of each frequency in the scan plane is analyzed utilizing the WSSE algorithm. Since the vibrational energy of the frequencies caused by the defect resonance is concentrated in the defect region, its energy distribution in the scan plane is non-uniform, resulting in a significant difference between the defect resonance frequencies’ SSE values and the other frequencies’ SSE values. This feature is used to estimate the resonant frequencies of internal defects. Ultimately, the defects are characterized based on the modal vibration patterns of the defect resonant frequencies. Tests were performed on two concrete blocks with simulated cavity defects, using an ultrasonic transducer as the excitation device to generate ultrasonic vibrations directly from the back of the blocks and applying an LDV as the acquisition device to collect vibration signals from their front sides. The results demonstrate the algorithm’s capacity to effectively pinpoint the information on the location and shape of shallow defects within the concrete, underscoring its practical significance for concrete internal defect detection in practical engineering scenarios.

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Section: Introductionmentioning

confidence: 99%

An Internal Defect Detection Algorithm for Concrete Blocks Based on Local Mean Decomposition-Singular Value Decomposition and Weighted Spatial-Spectral Entropy

et al. 2023

Entropy

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Squeeze-and-excitation attention and bi-directional feature pyramid network for filter screens surface detection

Xu,

Zhu,

Shi

et al. 2024

J. Electron. Imag.

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Based on the enhanced YOLOv5, a deep learning defect detection technique is presented to deal with the problem of inadequate effectiveness in manually detecting problems on the surface of filter screens. In the last layer of the backbone network, the method combines the squeeze-and-excitation attention mechanism module, the method assigns weights to image locations based on the channel domain perspective to obtain more feature information. It also compares the results with a simple, parameter-free attention model (SimAM), which is an attention mechanism without the channel domain, and the results are higher than SimAM 0.7%. In addition, the neck network replaces the basic PANet structure with the bi-directional feature pyramid network module, which introduces multi-scale feature fusion. The experimental results show that the improved YOLOv5 algorithm has an average defect detection accuracy of 97.7% on the dataset, which is 11.3%, 12.8%, 2%, 7.8%, 5.1%, and 1.3% higher than YOLOv3, faster R-CNN, YOLOv5, SSD, YOLOv7, and YOLOv8, respectively. It can quickly and accurately identify various defects on the surface of the filter, which has an outstanding contribution to the filter manufacturing industry.

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Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete

Beskopylny,

Stel’makh,

Shcherban’

et al. 2024

Sensors

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The search for structural and microstructural defects using simple human vision is associated with significant errors in determining voids, large pores, and violations of the integrity and compactness of particle packing in the micro- and macrostructure of concrete. Computer vision methods, in particular convolutional neural networks, have proven to be reliable tools for the automatic detection of defects during visual inspection of building structures. The study’s objective is to create and compare computer vision algorithms that use convolutional neural networks to identify and analyze damaged sections in concrete samples from different structures. Networks of the following architectures were selected for operation: U-Net, LinkNet, and PSPNet. The analyzed images are photos of concrete samples obtained by laboratory tests to assess the quality in terms of the defection of the integrity and compactness of the structure. During the implementation process, changes in quality metrics such as macro-averaged precision, recall, and F1-score, as well as IoU (Jaccard coefficient) and accuracy, were monitored. The best metrics were demonstrated by the U-Net model, supplemented by the cellular automaton algorithm: precision = 0.91, recall = 0.90, F1 = 0.91, IoU = 0.84, and accuracy = 0.90. The developed segmentation algorithms are universal and show a high quality in highlighting areas of interest under any shooting conditions and different volumes of defective zones, regardless of their localization. The automatization of the process of calculating the damage area and a recommendation in the “critical/uncritical” format can be used to assess the condition of concrete of various types of structures, adjust the formulation, and change the technological parameters of production.

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Concrete Multi-Type Defect Classification Algorithm Based on MSSMA-SVM

Cited by 3 publications

References 28 publications

An Internal Defect Detection Algorithm for Concrete Blocks Based on Local Mean Decomposition-Singular Value Decomposition and Weighted Spatial-Spectral Entropy

An Internal Defect Detection Algorithm for Concrete Blocks Based on Local Mean Decomposition-Singular Value Decomposition and Weighted Spatial-Spectral Entropy

Squeeze-and-excitation attention and bi-directional feature pyramid network for filter screens surface detection

Computer Vision Method for Automatic Detection of Microstructure Defects of Concrete

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