Edge Detection Technology Research Articles

Intelligent prediction of wear location and mechanism using image identification based on improved Faster R-CNN model

Exploring on-line wear status detection methods is of great significance to the intelligent operation and health management of mechanical equipment. At present, it is common to use high-resolution imaging equipment such as metallographic microscope and scanning electron microscope to determine the wear mechanism. However, its judgment relies on the subjective experience of the observer, and it is time-consuming and difficult to be implemented in on-line detection. In the present work, we proposed an image detection method based on improved Faster R-CNN model for wear location and wear mechanism identification. The model was trained and tested using a wear image data set produced and collected by a self-made tribometer equipped with an imaging system. The results showed that the proposed method had a detection accuracy of more than 99%, and its wear location and wear mechanism identification performance was superior to edge detection technology and Yolov3 target detection models. This work shed light on the development of a new approach for realizing on-line and intelligent wear status detection of machinery components.

Edge Detection technique based on HDR image quality assessment

Image edge detection is one of the major study aspects in current computer image processing field. The quality of the input images is uneven, some have large fuzzy areas, some are underexposed, and the edges of objects in the images are difficult to detect, and the application scenarios of image edge detection are limited. In the view of the above problems, this paper has proposed that by applying High Dynamic Range (HDR) image quality assessment technology, combining multiple images with different exposures into one HDR image with detailed edge information, This technology effectively solved problem of low edge information richness, improved the effectiveness of edge detection algorithms, and contributed to the development of edge detection technology.

Computed Tomography Imaging Based on Edge Detection Algorithm in Diagnosis and Rehabilitation Nursing of Stroke Patients with Motor Dysfunction

This study was to explore the effect of computed tomography (CT) images processed by image edge detection technology based on the improved Canny algorithm in the diagnosis of stroke patients with mobility dysfunction and to evaluate the clinical application value of early rehabilitation nursing (ERN). 114 patients who were diagnosed and treated in hospital and were suspected of having stroke movement dysfunction were selected as the research objects, and they were randomly divided into two groups, each with 57 patients. Patients in the control group were diagnosed with conventional CT examination, and the patients in observation group were diagnosed based on the CT images processed by the image edge detection technology based on the improved Canny algorithm. Patients in the observation group were divided into a group C and a group O. Patients (27 cases) in group O received rehabilitation training within 3 days after their vital signs were stabilized, and patients (30 cases) in group C received rehabilitation training within 3∼7 days after their condition was stabilized. The CT image diagnosis effects on patients of the control group and the observation group were analyzed, and the ERN effect on patients of the C group and the O group was compared. The results showed that the mean square error (MSE) of the improved Canny algorithm (233.78) was smaller than that of the traditional Canny algorithm and Sobel and Prewitt algorithm, and the peak signal-to-noise ratio (PSNR) (27.89) was greater than that of the traditional Canny algorithm and Sobel and Prewitt algorithm ( P < 0.05 ). Compared with the control group, the sensitivity (85.00% vs. 62.12%), specificity (70.59% vs. 36.36%), and accuracy (80.70% vs. 54.39%) of the examination method of the observation group were much higher ( P < 0.05 ). In addition, the total effective rate of patients in group O was 89.47%, which was greatly higher than that of group C (70.18%), and the scores of Meyer index and Barthel index were also higher in contrast to those of group C ( P < 0.05 ). In conclusion, the improved Canny algorithm showed a clearer display on the edge detection of CT images and good application effect. It showed the effect of making conventional CT more accurate in the examination and diagnosis of stroke patients, and it was worthy of clinical application and promotion. The research showed that the timelier rehabilitation training, the better the treatment effect of patients.

Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing

Information on the ground fissures induced by coal mining is important to the safety of coal mine production and the management of environment in the mining area. In order to identify these fissures timely and accurately, a new method was proposed in the present paper, which is based on an unmanned aerial vehicle (UAV) equipped with a visible light camera and an infrared camera. According to such equipment, edge detection technology was used to detect mining-induced ground fissures. Field experiments show high efficiency of the UAV in monitoring the mining-induced ground fissures. Furthermore, a reasonable time period between 3:00 am and 5:00 am under the studied conditions helps UAV infrared remote sensing identify fissures preferably. The Roberts operator, Sobel operator, Prewitt operator, Canny operator and Laplacian operator were tested to detect the fissures in the visible image, infrared image and fused image. An improved edge detection method was proposed which based on the Laplacian of Gaussian, Canny and mathematical morphology operators. The peak signal-to-noise rate, effective edge rate, Pratt’s figure of merit and F-measure indicated that the proposed method was superior to the other methods. In addition, the fissures in infrared images at different times can be accurately detected by the proposed method except at 7:00 am, 1:00 pm and 3:00 pm.

Research on image segmentation and edge detection technology based on computer vision

With the continuous development of computer vision technology, image segmentation technology and edge detection technology are becoming more and more mature. Firstly, this paper simulates the fixed threshold method and adaptive threshold method in image segmentation, and concludes that the fixed threshold method is applicable to images with obvious targets and backgrounds, and the adaptive threshold method is applicable to images with uneven distribution of light and dark, and secondly, the comparative experiment and result evaluation of edge detection of images by Sobel operator and Canny operator are used to conclude that the Sobel algorithm is more accurate in positioning edges, while Canny algorithm image edge information is more continuous and single edge response is better. Its edge detection is also better.

Experimental investigation on spark ignition of a staged partially premixed annular combustor

In this paper, the ignition transient process is experimentally investigated on a staged partially premixed annular combustor. This work focuses on the impact of bulk velocity and equivalence ratio on light-round speed and characteristic of flame propagation during light-round process. The set-up consists of two concentric transparent torus quartz walls and it is equipped with an air-curtain anti-pollution reflector above the combustor tilts at a 45° angle from the vertical, which allows visualization of the flame from both the side and the top of the combustor. High-speed images of the spontaneous flame emission with 5 kHz from the top view and 10 kHz from the side view are recorded. Image edge detection technology is used to analyze the flame front and leading point dynamics. Radial and azimuthal oscillations of the leading point trajectory indicate that there are the wrinkled turbulent flame fronts. The large span of flame fragment azimuthal propagation is due to swirling air in the combustor. It is found that the light-round process can be decomposed into three different distinct phases according to the light-round speed. The figures of light-round time and speed against equivalence ratio illustrate that bulk velocity and equivalence ratio play a positive role in light-round process under certain conditions (stochastic zone, linear descent zone). It is also confirmed that the ignition process is significantly impacted by the spray characteristics. The results presented in this paper bring new insights into the ignition of realistic gas turbine with centrally-staged combustor.

An edge detection–based eGAN model for connectivity in ambient intelligence environments

In generative adversarial networks (GANs), a generator network and discriminator network compete in deep learning tasks to generate real images. To reduce the difference between the generated image and actual image, an edge GAN (eGAN) model using edge detection was proposed. This eGAN model can utilize ambient intelligence, a human-centered technology that includes IoT, smart cities, and autonomous driving. Ambient intelligence is essential for the interconnection between humans and objects. The eGAN model was used to make this connectivity more accurate and reliable. Edge detection is an edge feature that extracts the boundaries of an image and generate images in a fast manner; however, because its threshold is arbitrarily set, the connectivity may be unstable. To solve this problem and improve the performance of the eGAN model, we analyzed various GAN models and edge detection methods and proposed a new edge detection technology using threshold settings. This edge detection method sets the threshold value for images, thereby increasing the accuracy of edge connection and reducing the loss error between the image generated by the eGAN model and actual image. To evaluate the performance of the eGAN model, the error between the generated image and actual image was compared by applying the GAN and eGAN models to the same image dataset. Consequently, it was found that the performance of the eGAN model improved by 21% in comparison to the existing GAN model.

A Review of the Edge Detection Technology

The edge detection-based has profoundly inspired recent works in image classification, object detection, segmentation, et al. With the growth of computer vision, the performance of edge detection has been notably improved. In this paper, we concentrate on presenting some edge detection technologies and grouping them into two major categories: classical edge detection technology and deep learning-based edge detection technology. For every team in the classification, the fundamental thoughts are first described, and then we probe beneath into the mainly related literature in recent years. Furthermore, the evaluation indicators are entirely elaborated. In addition, this conclusion is given by a comprehensive summary which provides suggestions on future perspectives.

Research on Simulation Rendering Technology of Watercolor Painting Based on Canny Edge Darkening

Watercolor painting is a branch of traditional painting art, which has a history of more than years of development. Watercolor painting has been widely used and popularized because of its bright colors, transparency, rich expression techniques, strong expressiveness, and simple production tools and materials. The artistic aesthetics of watercolor painting has a wide audience base, which is the premise of the research of watercolor simulation drawing. Edge detection technology is the basis of image measurement, image segmentation, image compression and pattern recognition and other image processing technologies, as well as the technical basis of watercolor digital rendering. Based on the Canny algorithm, this paper proposes a watercolor simulation drawing method based on Canny edge detection. From the experimental results, this method has a good simulation rendering effect.

Inverse algorithm for out-of-plane deformation in shearography

Shearography directly measures the first derivative of the object deformation, but cannot directly obtain the deformation distribution of the object, which brings much inconvenience to the analysis of the mechanical properties of the material. This paper introduces a phase reconstruction method based on image-side shearing amount and shearing direction, which can inversely calculate the object’s out-of-plane deformation distribution. The imaging area and imaging center of the measured object are determined through image segmentation and edge detection technology, and the shearing size and direction as well as the interference area can be accurately extracted. The experimental results show that the method can calculate the out-of-plane deformation distribution of shearography and the relative error of the maximum deformation value is about 1.65%.

Measuring the Ocular Morphological Parameters of Guinea Pig Eye with Edge Detection and Curve Fitting.

Aim To identify the guinea pig eyeball with edge detection and curve fitting and devise a noncontact technology of measuring ocular morphological parameters of small experimental animal. Methods Thirty-nine eyeballs of guinea pig eyeballs were photographed to obtain the anterior and posterior surface; transverse and sagittal planes after the eyeballs were eviscerated. Next, the eyeball photos were input into digital image analysis software; the corresponding photo pixels-actual length ratio was acquired by a proportional scale. The contour lines of the eyeballs were identified by edge detection technology; conic curve fitting was applied to fit the contour line of the eyeball. The maximum and minimum diameters, the horizontal and vertical diameters, eccentricity, tilt angle, cross-sectional area, equatorial circumference, retrobulbar equatorial maximum length, corneal radius of curvature (CRC) in central region, and the whole cornea were calculated according to the geometric principles. The corneal data of in vitro study were compared with the in vivo results. Results The contour line of the selected guinea pig eye was identified correctly by edge detection. There were no significant differences between anterior and posterior surfaces of one eyeball in the maximum diameters, eccentricity, cross-sectional area, equatorial circumference, and tilt angle (P > 0.01). There were significant differences of eccentricity and CRC between central region and the whole cornea (P < 0.01). There were no significant differences between keratometer in vivo and cornea in vitro. Conclusion It was feasible to measure an experimental animal eye in a noncontact way. Edge detection and curve fitting technology could accurately evaluate the ocular morphological parameters.

Adaptive Image Edge Extraction Based on Discrete Algorithm and Classical Canny Operator

In order to improve the accuracy of image edge detection, this paper studies the adaptive image edge detection technology based on discrete algorithm and classical Canny operator. First, the traditional sub-pixel edge detection method is illustrated based on the related literature research. Then, Canny operator is used for detection, the edge model of the quadric curve is established using discrete data, and the adaptive image edge parameters are obtained using one-dimensional gray moment. Experimental results show that the accuracy of feature detection is 99%, which can be applied to the practice of image edge detection to a certain extent.

Numerical simulation of three dimensional flow in Yazidang Reservoir based on image processing

In order to study the water flow movement of the Yazidang Reservoir, this paper generates the initial terrain for the researched water area with the image stitching technology and image edge detection technology, establishes a 3D k - ɛ mathematical model, solves the equations discretely by FVM and SIMPLEC algorithms, studies the numerical simulation of the water flow movement of the reservoir under four working conditions, and analyzes the flow field on the surface and at the bottom of the reservoir. The results show the improved terrain pre-processing accuracy and efficiency of the researched water area and the rationality of the water flow field and rate simulation results, which means that the established 3D turbulence mathematical model can be applied to the numerical simulation of the reservoirs similar to the Yazidang Reservoir. The numerical simulation of 3D turbulence in Yazidang Reservoir provides a theoretical basis and practical application value for the numerical simulation of similar reservoirs.

Research on Edge Detection Method Based on Improved HED Network

Aiming at the problem of rough and fuzzy edges generated by the current edge detection technology based on HED network, an improved edge detection method for HED network is proposed. First, the useful information captured by each convolutional layer of HED becomes rougher as the size of the acceptance field increases. The improved HED network makes use of all the information of the convolution layer to capture more targets in a larger range, or make the local boundaries of the targets possible. The improved HED makes full use of the multi-scale and multi-level information of the target to obtain high-precision and high-quality edge maps, which lays a good foundation for image segmentation.

FPGA Based Bone Fracture Detector

Medical imaging was existed as a new domain of research for the treatment of images that can be easily improved, cut and exposed. The proposed technique aims at detecting the objects’ boundaries of noisy images by means of the information on the images of X-ray. Since medical images are made up of noise, and since the edges of the bone make it difficult to detect, edge detection technology has been a key tool in discovering and improving these edges. The edges are changes in the intensity of the lighting of the image. Among several methods for edge detection, the Sobel method is used in this research paper as it is the most famous operator used in the algorithms of detection edges. The algorithms of image processing have been confined to the implementation of slow software due to the limited speed of the processor. As such, a dedicated processor for edge detection was designed and implemented. The results were compared with those arrived at by MATLAB. They were found out to be the same as those of implementation on FPGA which showed the way that Sobel edge detection can assist in determining the location of the fractures in X-ray images. A speedup of approximately 153.8586 can be obtained by Spartan3E FPGA XC3S500E kit system over the software implementation.

A Review Paper on License Plate Recognition System

ANPR provides solution in which the steps to run an efficient intelligent transport network might be taken. Owing to the rapid increase in vehicles it has become a requirement for traffic control management. ANPR's main goal is to track traffic and for the purpose of defense. Number plate recognition uses image processing techniques or OCR techniques and edge detection technology to detect characters on license plates. The model comprises of three modules which are module for car detection, module for license plate segmentation and module for recognition. Starting from auto robberies, violating traffic laws, to law enforcement administration, Image processing gave us a determination to put a stop to these violations. This review paper provided an examination of the different license plate recognition design implemented so far.

Research on Fault Detection Algorithm of Pantograph Based on Edge Computing Image Processing

Traditional pantograph detection technology has installed a large number of various types of sensors on the pantograph, which is not conducive to the flow characteristics of the pantograph. Traditional detection technology can neither monitor the operation status of the pantograph in real time, nor guarantee the safe operation of the entire train. The existing schemes are only manual solutions, and intelligent real-time monitoring is performed without error, and a lot of labor costs are required. Focusing on the shortcomings of traditional pantographs in sliding plate wear and sliding plate crack detection, an improved mean filtering algorithm is proposed to optimize the image of pantograph, and the adaptive Canny edge detection technology is used to accurately calculate the skateboard wear. Firstly, the improved image algorithm is used to pre-treat the pantograph slide wear and crack length, then the improved mean filtering algorithm is combined with adaptive Canny operator for image optimization detection, and finally the edge calculation is used for crack identification. An optimized real-time on-line detection technology is proposed, which can accurately measure the wear and tear of skateboards and cracks. For different experimental environments, the optimization algorithm proposed in this paper is used to detect pantographs in real time. The experimental results show that, compared with the traditional detection algorithm, the detection accuracy of sliding plate wear can reach 0.5 mm, and and that of sliding plate cracks can reach 0.4 mm. The crack recognition accuracy of skates is as high as 93%.

Research on temperature distribution law around crack using the moving mode of eddy current thermography

Eddy current thermography (ECT) plays an important role in the field of nondestructive testing (NDT). In this study, a moving detection mode of ECT is used for crack detection and the temperature distribution near the crack in this mode is studied. Firstly, the numerical simulation is used to analyze the eddy current distribution on the specimen surface. And then the experimental studies are carried out and thermal images are obtained in the experiments. The surface temperature distribution of the specimen is analyzed, and the fitted curve of the temperature decline trend is given. The curve is helpful to determine the best detection area. The temperature distribution characteristics near the crack are analyzed and the position of the largest temperature difference on the specimen surface is determined. Finally, a new method for determination of crack opening size based on edge detection technology is proposed, and the size of the crack opening is obtained.

Texture Construction Edge Detection Algorithm

The edge detection algorithm is the cornerstone of image processing; a good edge detection result can further extract the required information through rich texture information and achieve object detection, segmentation, and identification. To obtain a rich texture edge detection technology, this paper proposes using edge texture change for edge construction and constructs the edge contour through constructing an edge texture extension between the blocks to reduce the missing edge problem caused by the threshold setting. Finally, through verification of the experimental results, the proposed method can effectively overcome the problem caused by unsuitable threshold setting and detect rich object edge information compared to the adaptive edge detection method.

Convolutional block attention module based on visual mechanism for robot image edge detection

In recent years, with the continuous development of computer vision, digital image and other information technology, its application in robot image has attracted many domestic and foreign scholars to conduct researches. Edge detection technology based on traditional deep learning