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

콘크리트 진동 상태를 식별하기 위한 방향성 기울기 히스토그램 및 SVM

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.

저자 스포트라이트: 방향성 그래디언트 히스토그램 기법을 사용한 효율적인 이미지 인식 및 벡터 머신 지원 

Support Vector Machine을 기반으로 한 콘크리트 진동 상태의 이미지 인식 및 파라미터 분석

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  

구체적인 이미지 샘플의 회색 이진화

구체적인 영상을 획득한 후 MATLAB의 imread 함수를 사용하여 JPEG 파일을 3개의 색 채널을 포함하는 1024픽셀 x 1024픽셀의 행렬로 처리합니다. 다음으로, MATLAB 함수 rgb2gray를 적용하여 영상을 회색조로 변환합니다. 다음 방정식에 따라 각 픽셀의 회색 값을 계산하고, 회색 값을 1024×1024 논리형 형식의 uint8 형식 데이터로 저장합니다.그런 다음 픽셀 임계값 theta보다 큰 회색 값의 이진 값을 1로 조정하고 theta 픽셀보다 작은 회색 값의 이진 값을 0으로 설정합니다. 그런 다음 다양한 구체적인 샘플 진동 상태에 대해 이진화 임계값이 50, 100, 150, 200인 1024 x 1024 형식으로 결과를 저장합니다. 이진화 임계값이 감소함에 따라 진동되지 않은 콘크리트 이미지 샘플의 이진 이미지에서 흰색 영역이 크게 감소합니다.이진화 임계값 250에서는 이진 영상이 순수한 검은색으로 나타납니다. 이진화 임계값이 있는 진동 콘크리트 샘플의 이미지는 진동되지 않은 콘크리트 샘플과 같습니다. 그러나 백색 영역의 감소는 진동하는 콘크리트 이미지 샘플에서 더 두드러집니다.진동 콘크리트의 바이너리 회색 이미지는 이진화 임계값이 감소함에 따라 감소합니다.

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 ...

연구

공학

Concrete Sample Image Acquisition to Extract Concrete Characteristics

구체적인 특성을 추출하기 위한 구체적인 샘플 이미지 획득

Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training

서포트 벡터 기계 학습을 위한 방향성 기울기 고유값 계산

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

콘크리트의 진동 상태를 식별하기 위한 SVM(Support Vector Machine) 교육