Name: Gray Binarization of Concrete Image Sample
Uploaded: 2024-01-05T13:50:00
Description: Watch this Scientific Journal Video about Gray Binarization of Concrete Image Sample at JoVE.com

gray binarization of concrete image sample

Histograma de gradiente direcional e SVM para identificação de estados de vibração do concreto

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.

Autor em destaque: Reconhecimento eficiente de imagens usando a técnica de histograma de gradiente direcional e máquinas de vetores de suporte 

Reconhecimento de Imagem e Análise de Parametrização do Estado de Vibração do Concreto Baseado em Máquina de Vetor de Suporte

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  

Binarização Cinza de Amostra de Imagem Concreta

Depois de adquirir as imagens concretas, use a função imread do software MATLAB para processar o arquivo JPEG em uma matriz de 1024 pixels por 1024 pixels cobrindo 3 canais de cores. Em seguida, aplique a função MATLAB rgb2gray para converter a imagem em escala de cinza. Calcule o valor de cinza de cada pixel de acordo com essa equação e salve o valor de cinza como os dados de formato lógico uint8 de 1024 por 1024.Em seguida, ajuste o valor binário dos valores de cinza que são maiores que o limite de pixel theta para um e defina o valor binário de valores de cinza menor que o pixel theta como zero. Em seguida, salve os resultados no formato 1024 por 1024 com limites de binarização de 50, 100, 150 e 200 para vários estados de vibração de amostra concreta. À medida que o limiar de binarização diminui, a área branca na imagem binária da amostra de imagem de concreto não vibrada diminui significativamente.Em um limite de binarização de 250, a imagem binária aparece em preto puro. A imagem da amostra de concreto vibratória com o limiar de binarização é como a amostra de concreto não vibrado. Mas a redução da área branca é mais pronunciada na amostra de imagem de concreto vibratório.A imagem binária cinza do concreto vibrado diminui com a diminuição do limiar de binarização.

Research

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 ...

Engenharia

Concrete Sample Image Acquisition to Extract Concrete Characteristics

Aquisição de Imagem de Amostra de Concreto para Extração das Características do Concreto

Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training

Cálculo do Autovalor do Gradiente Direcional para Treinamento de Máquina de Vetor de Suporte

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

Treinamento de máquina vetorial de suporte (SVM) para identificar o estado de vibração do concreto