Name: Gray Binarization of Concrete Image Sample
Uploaded: 2024-01-05T13:50:00
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gray binarization of concrete image sample

Directioneel gradiënthistogram en SVM voor het identificeren van trillingstoestanden van beton

Concrete is a fundamental building material extensively used in the construction industry. During pumping, the concrete frequently develops voids that require compaction through vibration. Inadequate vibration may result in a honeycombed concrete surface, while excessive vibration can lead to concrete segregation1,2. The quality of vibration operation significantly impacts the strength3,4,5,6 and durability of the formed concrete structures7,8. Cai et al.9,10 conducted a study that combined experimental research with numerical analysis to investigate the influence mechanism of vibration on aggregate settlement and concrete durability. The findings revealed that vibration time and aggregate particles exert a substantial impact on aggregate settlement, while aggregate density and the plastic viscosity of the cement-based material have minimal effects. Vibration causes aggregate deposition at the bottom of the concrete specimens. Moreover, as the vibration time increases, the chloride ion concentration decreases at the bottom of the concrete specimens while significantly increasing at the top9,10.
Currently, the assessment of concrete vibration state relies predominantly on manual judgment. As the construction industry continues to progress through intelligent reforms, robot operations have emerged as the future direction11,12. Consequently, a crucial challenge in intelligent vibration operations is how to enable robots to identify the vibration state of concrete.
The histogram of the oriented gradient is a technique that utilizes the intensity gradient of pixels or the distribution of edge directions as a descriptor to characterize the representation and shape of objects in images13,14. This approach operates on the local grid cells of the image, providing robust stability in characterizing image changes under various geometric and optical conditions.
Zhou et al.15 proposed a method for directly extracting directional gradient features from Bayer mode images. This approach omits numerous steps in calculating the directional gradient by matching the color filter column with the gradient operator, thereby significantly reducing the computational requirements for directional gradient image recognition. He et al.16 utilized the directional gradient histogram as the underlying feature and employed the mean clustering algorithm to classify rail fasteners and determine whether the fasteners are defective. The recognition results indicated that the histogram of the oriented gradient feature exhibited high sensitivity to fastener defects, meeting the needs of railway maintenance and repair. In another study, Xu et al.17 preprocessed face image features using Gabor wavelet filtering and reduced the dimension of feature vectors through binary coding and the HOG algorithm. The average recognition accuracy of the method is 92.5%.
The support vector machine (SVM)18 is used to map the vector into a high-dimensional space and establishes a separating hyperplane with a suitable direction to maximize the distance between two parallel hyperplanes. This allows for the classification of support vectors19. Scholars have improved and optimized this classification technology, leading to its application in various fields such as image recognition20,21, text classification22, reliability prediction23, and fault diagnosis24.
Li et al.25 developed a two-stage SVM model for seismic failure pattern recognition, focusing on three seismic failure modes. The analysis results indicate that the proposed two-stage SVM method can achieve more than 90% accuracy for the three failure modes. Yang et al.26 integrated an optimization algorithm with the SVM to simulate the relationship between the five ultrasonic parameters and the stress of the loaded concrete. The performance of an unoptimized SVM is unsatisfactory, particularly in the low-stress stage. However, traversing the model optimized by the algorithm yields improved results, albeit with lengthy computation times. In comparison, the particle swarm optimization optimized SVM significantly reduces the calculation time while delivering optimal simulation results. Yan et al.27 employed SVM technology and introduced a precision-insensitive loss function to predict the elastic modulus of high-strength concrete, comparing its prediction accuracy against the traditional regression model and neural network model. The research findings demonstrate that the SVM technology produces a smaller prediction error for elastic modulus compared to other methods.
This paper collects image samples of concrete under various vibration states and describes the concrete&#39;s different states using the directional gradient histogram technique. The directional gradient is employed as a feature vector for training the SVM, and the study focuses on the viability of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. Additionally, the paper analyzes the influence mechanism between three key parameters-binarization threshold, directional gradient statistical block size, and directional gradient statistical interval number-in the feature extraction process of the directional gradient histogram and the recognition accuracy of the SVM.

Author Spotlight: Efficiënte beeldherkenning met behulp van directionele gradiënthistogramtechniek en ondersteunende vectormachines 

Beeldherkenning en parameteranalyse van de trillingstoestand van beton op basis van de ondersteuningsvectormachine

The protocol described in this paper utilized the directional gradient histogram technique to extract the characteristics of a concrete image samples under various vibration cells. It employs a support vector machine for machine learning, resulting in imaging recognition method with minimal trained sample requirements and low computer performance demands. This approach significantly reduce the number of samples required, and lowers the computer performance requirements.With a laptop equivalent at 2.3 gigahertz central processing units, the recognition process completes the train space differentiation of the supported vector machine within just 50 seconds. The image segmentation below size of 128 projects and 128 projects is utilized. The number of directional vectors for statistical angle inverse is set to 12.In the 224 resolution image process, the best recognition occurrence for machine learning result is achieved.

Watch this Scientific Journal Video about Gray Binarization of Concrete Image Sample at JoVE.com

Gray Binarization of Concrete Image Sample  

Grijze binarisering van concreet beeldvoorbeeld

Gebruik na het verkrijgen van de concrete afbeeldingen de imread-functie van MATLAB-software om het JPEG-bestand te verwerken tot een matrix van 1024 pixels bij 1024 pixels die 3 kleurkanalen bestrijken. Pas vervolgens de MATLAB-functie toe rgb2gray om de afbeelding naar grijswaarden te converteren. Bereken de grijswaarde van elke pixel volgens deze vergelijking en sla de grijswaarde op als de uint8-indelingsgegevens van 1024 bij 1024 logische indelingsgegevens.Pas vervolgens de binaire waarde van de grijswaarden die groter zijn dan de pixeldrempel theta aan op één en stel de binaire waarde van grijswaarden kleiner dan de theta-pixel in op nul. Sla vervolgens de resultaten op in de indeling 1024 bij 1024 met binarisatiedrempels van 50, 100, 150 en 200 voor verschillende trillingstoestanden van betonmonsters. Naarmate de binarisatiedrempel afneemt, neemt het witte gebied in het binaire beeld van het niet-getrilde betonbeeldmonster aanzienlijk af.Bij een binarisatiedrempel van 250 lijkt het binaire beeld puur zwart. Het beeld van het trillende betonmonster met de binarisatiedrempel is vergelijkbaar met het niet-getrilde betonmonster. Maar de vermindering van het witte gebied is meer uitgesproken in het trillende betonbeeldmonster.Het binaire grijze beeld van trilbeton neemt af met de afnemende binarisatiedrempel.

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JoVE Journal

Engineering

Gray Binarization of Concrete Image Sample

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

In this paper, the directional gradient histogram technology is employed to extract the features of concrete image samples captured under different ...

Concrete Sample Image Acquisition to Extract Concrete Characteristics

Beeldacquisitie van concrete voorbeelden om concrete kenmerken te extraheren

Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training

Berekening van de eigenwaarde van de directionele gradiënt voor training van ondersteuningsvectormachines

Support Vector Machine (SVM) Training to Identify the Vibration State of Concrete

Support Vector Machine (SVM)-training om de trillingstoestand van beton te identificeren