gray binarization of concrete image sample

Directional Gradient Histogram and SVM for Identifying Concrete Vibration States

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: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

Bilderkennung und Parameteranalyse des Betonschwingungszustandes auf Basis der Support Vector Machine

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Gray Binarization of Concrete Image Sample  

Graue Binarisierung der konkreten Bildprobe

Nachdem Sie die konkreten Bilder erfasst haben, verwenden Sie die Imread-Funktion der MATLAB-Software, um die JPEG-Datei in eine Matrix von 1024 x 1024 Pixeln zu verarbeiten, die 3 Farbkanäle abdeckt. Wenden Sie als Nächstes die MATLAB-Funktion rgb2gray an, um das Bild in Graustufen umzuwandeln. Berechnen Sie den Grauwert jedes Pixels gemäß dieser Gleichung, und speichern Sie den Grauwert als uint8-Formatdaten von 1024 x 1024 logischen Formatdaten.Passen Sie als Nächstes den Binärwert der Grauwerte, die größer als der Pixelschwellenwert von Theta sind, auf eins an, und setzen Sie den Binärwert von Grauwerten, die kleiner als das Theta-Pixel sind, auf Null. Speichern Sie dann die Ergebnisse im Format 1024 x 1024 mit Binarisierungsschwellen von 50, 100, 150 und 200 für verschiedene Schwingungszustände von Betonproben. Wenn die Binarisierungsschwelle abnimmt, verringert sich der weiße Bereich im Binärbild der nicht vibrierenden Betonbildprobe erheblich.Bei einer Binarisierungsschwelle von 250 erscheint das Binärbild rein schwarz. Das Bild der vibrierenden Betonprobe mit der Binarisierungsschwelle ist wie die der nicht vibrierten Betonprobe. Die Verkleinerung der weißen Fläche ist jedoch in der vibrierenden Betonbildprobe ausgeprägter.Das binäre graue Bild von Rüttelbeton nimmt mit abnehmender Binarisierungsschwelle ab.

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Gray Binarization of Concrete Image Sample

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80 Aufrufe. Nachdem Sie die konkreten Bilder erfasst haben, verwenden Sie die Imread-Funktion der MATLAB-Software, um die JPEG-Datei in eine Matrix von 1024 x 1024 Pixeln zu verarbeiten, die 3 Farbkanäle abdeckt. Wenden Sie als Nächstes die MATLAB-Funktion rgb2gray an, um das Bild in Graustufen umzuwandeln. Berechnen Sie den Grauwert jedes Pixels gemäß dieser Gleichung, und speichern Sie den Grauwert als uint8-Formatdaten von 1024 x 1024 logischen Formatdaten.Passen Sie als Nächstes den Binä...

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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 vibration states. The support vector machine (SVM) is utilized to learn the relationship between image features and vibration state. The machine learning results are subsequently used to assess the feasibility of the concrete vibration state. Simultaneously, the influence mechanism of the calculation parameters of the directional gradient histogram on the recognition accuracy is analyzed. The results demonstrate the feasibility of using the directional gradient histogram-SVM technology to identify the vibration state of concrete. The recognition accuracy initially increases and then decreases as the block size of the directional gradient, or the number of statistical intervals increases. The recognition accuracy also decreases linearly with the increase of the binarization threshold. By using sample images with a resolution of 1024 pixels x 1024 pixels and optimizing the feature extraction parameters, a recognition accuracy of 100% can be attained.

The protocol described in this paper utilizes the directional gradient histogram technique to extract the characteristics of concrete image samples under various vibration states. It employs a support vector machine for machine learning, resulting in an image recognition method with minimal training sample requirements and low computer performance demands.

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

Forschung

Ingenieurwesen

Concrete Sample Image Acquisition to Extract Concrete Characteristics

Aufnahme von Betonprobenbildern zur Extraktion von Betonmerkmalen

Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training

Berechnung des Eigenwerts des Richtungsgradienten für das Training von Support-Vektormaschinen

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

Support Vector Machine (SVM) Training zur Identifizierung des Schwingungszustands von Beton