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

コンクリートの振動状態を特定するための指向性勾配ヒストグラムと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.

著者スポットライト:Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines 

サポートベクターマシンによるコンクリート振動状態の画像認識とパラメータ解析

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  

コンクリート画像サンプルのグレイ二値化

具体的な画像を取得した後、MATLAB ソフトウェアの関数 imread を使用して、JPEG ファイルを 3 つのカラー チャネルをカバーする 1024 ピクセル x 1024 ピクセルの行列に処理します。次に、MATLAB 関数 rgb2gray を適用して、イメージをグレースケールに変換します。この式に従って各画素のグレー値を計算し、グレイ値を1024×1024の論理フォーマットデータのuint8フォーマットデータとして保存します。次に、ピクセル閾値 θ より大きいグレー値のバイナリ値を 1 に調整し、シータ ピクセルより小さいグレー値のバイナリ値を 0 に設定します。次に、さまざまなコンクリート サンプルの振動状態について、50、100、150、および 200 のバイナリ化しきい値を使用して、結果を 1024 x 1024 形式で保存します。二値化閾値が小さくなると、非振動コンクリート画像サンプルのバイナリ画像中の白い領域が有意に減少する。2 値化のしきい値が 250 の場合、バイナリ イメージは純粋な黒で表示されます。二値化閾値を持つ振動コンクリート試料の画像は、非振動コンクリート試料と同様である。しかし、白い領域の減少は、振動するコンクリート画像サンプルでより顕著です。振動したコンクリートのバイナリ グレー イメージは、二値化しきい値が減少するにつれて減少します。

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 ...

工学

Concrete Sample Image Acquisition to Extract Concrete Characteristics

コンクリート特性を抽出するためのコンクリートサンプル画像取得

Calculation of Directional Gradient Eigenvalue for Support Vector Machine Training

サポートベクターマシン学習のための方向勾配固有値の計算

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

コンクリートの振動状態を識別するためのサポートベクターマシン(SVM)トレーニング