Digital Image Recognition Technology Research Articles

Microstructure size and packing of fracture surface on the nonlinear breakage mechanical response of different marbles under cyclic loading

The microstructural properties of fracture surfaces are a remarkable reflection of the nonlinear mechanical properties of rocks, which have not been thoroughly evaluated to date. In order to understand the contributions of microstructure size and packing to the nonlinear mechanical response, both micro- (field emission scanning electron microscopy (SEM) experiments on failure specimen section) and macro-scale (unequal amplitude cycle experiment) tests were carried out on different marbles. The size and arrangement properties of microstructures on the fracture surface were determined using digital image recognition technology based on micro-scale experiments. Remarkable differences were observed among different types of marble in terms of breakage evolution, fracture combining form, and nonlinear evolution of energy. The micro-deformation of marbles is characterized by the deformation distribution and microstructure inhomogeneity in strong deformation zones of failure. The microstructures of brittle-ruptured marbles exhibit mechanical fragmentation and fracture of mineral grains. The ductile deformation of marble is characterized by the presence of solid flow traces within the mineral crystals, as well as the loose arrangement of mineral crystal grains in the strongly deformed zones. The constitutive model for marble samples with fine mineral crystals can be derived by nonlinearly separating the Helmholtz free energy during the breakage process and defining the relationship between breakage force and dissipated energy in the absence of residual strength after fracture. The obtained nonlinear breakage constitutive equation is in good agreement with the experimental results. These findings may provide basis and inspiration for engineering construction and geological disaster prevention.

Dynamic Failure Characteristics of Sandstone Containing Different Angles of Pre-Existing Crack Defects

Fracture within the rock is one of the main factors leading to rock destabilization and has a significant effect on the stability of the project. In this study, sandstone is used as a research target, specimens with crack inclination angles of 0°, 30°, 45°, 60°, and 90° are prefabricated, and the split Hopkinson pressure bar (SHPB) impact test of sandstone with cracks is carried out based on digital image recognition technology to explore the dynamic damage characteristics of the specimens with five angles. The basic mechanical parameters of sandstone are tested to determine the RHT model intrinsic parameters, and the numerical computational RHT model of sandstone containing crack defects is established, which is verified in comparison with the test to analyze the validity of the model. Finally, the failure characteristics of the numerical model under initial stress were carried out. The study shows the following: with the increase in the fracture angle, the dynamic compressive strength and deformation modulus are distributed in a slanting V-shape, and the inclination angle of 45° is the smallest. The strain rate and energy dissipation rate are distributed in a slanting N-shape, and the inclination angle of 45° is the largest. The transmittance shows a decreasing trend, which is the opposite of the reflectivity pattern. The crack angle determines the location and direction of the initial crack, which affects the failure mode. In addition, the parameters of the RHT constitutive model suitable for sandstone are obtained, and the damage and strength patterns of the established RHT model are highly consistent with the laboratory test results. The damage range of numerical models for crack defects with different inclination angles is negatively correlated with confining pressure values and positively correlated with axial pressure values. The damage zones are symmetrically distributed approximately perpendicular to the direction of cracks, and the confining pressure has a contributing role in the peak of the element stresses; however, the axial compression has no contribution in the peak of the element stresses.

Insulator Fault Detection in Aerial Images based on the Mixed-grouped Fire Single-shot Multibox Detector

Abstract In a complex background, insulator fault is the main factor behind transmission accidents. With the wide application of unmanned aerial vehicle (UAV) photography, digital image recognition technology has been further developed to detect the position and fault of insulators. There are two mainstream methods based on deep learning: the first is the “two-stage” example for a region convolutional neural network and the second is the “one-stage” example such as a single-shot multibox detector (SSD), both of which pose many difficulties and challenges. However, due to the complex background and various types of insulators, few researchers apply the “two-stage” method for the detection of insulator faults in aerial images. Moreover, the detection performance of “one-stage” methods is poor for small targets because of the smaller scope of vision and lower accuracy in target detection. In this article, the authors propose an accurate and real-time method for small object detection, an example for insulator location, and its fault inspection based on a mixed-grouped fire single-shot multibox detector (MGFSSD). Based on SSD and deconvolutional single-shot detector (DSSD) networks, the MGFSSD algorithm solves the problems of inaccurate recognition in small objects of the SSD and complex structure and long running time of the DSSD. To resolve the problems of some target repeated detection and small-target missing detection of the original SSD, the authors describe how to design an effective and lightweight feature fusion module to improve the performance of traditional SSDs so that the classifier network can take full advantage of the relationship between the pyramid layer features without changing the base network closest to the input data. The data processing results show that the method can effectively detect insulator faults. The average detection accuracy of insulator faults is 92.4% and the average recall rate is 91.2%.

Finite Element Modeling of Asphalt Mixture Based on Image Recognition

This study describes a method for establishing a mechanical model of heterogeneous finite element method. With the combination of digital image recognition technology and the theory of automatic mesh generation, it is possible to establish an inhomogeneous finite element model for mechanical calculation. Firstly, the micro-structure of the two-dimensional mixture is identified. Then all the boundary points are imported into ANSYS. Finally, the finite element mesh model of the micro-structure of the asphalt mixture is obtained by using the automatic finite element mesh generation technology. Taking Marshall specimen as an example, meshing the model proves that the method is feasible.

Automatic Identification of Butterfly Species Based on Gray-Level Co-occurrence Matrix Features of Image Block

In recent years, automatic identification of butterfly species arouses more and more attention in different areas. Because most of their larvae are pests, this research is not only meaningful for the popularization of science but also important to the agricultural production and the environment. Texture as a notable feature is widely used in digital image recognition technology; for describing the texture, an extremely effective method, graylevel co-occurrence matrix (GLCM), has been proposed and used in automatic identification systems. However, according to most of the existing works, GLCM is computed by the whole image, which likely misses some important features in local areas. To solve this problem, this paper presents a new method based on the GLCM features extruded from three image blocks, and a weight-based k-nearest neighbor (KNN) search algorithm used for classifier design. With this method, a butterfly classification system works on ten butterfly species which are hard to identify by shape features. The final identification accuracy is 98%.

Building Materials Defect Monitoring Based on Digital Image Recognition Technology

On the basis of using the vacuum epoxy impregnation technology to obtain microscopic images of building materials, algorithms such as image scaling, index image, image recognition and image identification are respectively designed to identify the pores of the building materials through programs. The images zooming adopts bilinear interpolation to realize the lossless compression of the original microscopic image. The identification of the aperture adopts the method of brightness screening, and the operation of morphological opening and filling. Through the identification of the pores, the material defects can be further analyzed by mesomechanics.

一种基于数字图像处理的粗指针仪表读数识别方法

变电站中变压器上的油位表盘是监测变压器运行状态的关键设备，利用数字图像识别技术是准确获取油位表盘指针读数的有效途径之一。针对基于图像处理的油位表盘读数系统中存在的精度不够、可靠性低和实时性不好的问题，根据油位表指针的图像特征，提出一种基于连通区域面积大小先验知识的图像分割算法。并在该算法基础上实现变压器油位表盘读数的方法。由Matlab仿真实验表明，该方法能较好地从图像背景中分割油位表盘指针图像，为准确判定表盘指针读数奠定了基础，也为指针仪表读数识别提供了一种实用方法。 The oil-level dial of transformer is the key device to detect the state of transformers in the transformer substation, and the digital image recognition technology is an effective way to accurately obtain the oil-level dial’s numerical reading. As to the inaccuracy, low reliability and bad real-time performance of the traditional image-based dial reading systems, according to the image features of oil-level dial, a segmentation algorithm based on the future knowledge of the connected region’s area is proposed. And based on this algorithm an oil-level dial reading method is implemented. The experimental results of Matlab simulation show that our method is better to segment the pointer from the background of the oil-level dial images, laying a foundation to accurately judge the reading of the dial’s pointer, furthermore it provides a practical way to recognize the oil-level dial’s numerical reading.