Edge Detection Operator Research Articles

Identification of asphalt pavement transverse cracking based on 2D reconstruction of vehicle vibration signal and edge detection algorithm

Transverse cracking is related to asphalt pavement structural performance. However, the method for transverse cracks identification by vehicle vibration signal has hardly been applied because of an extremely low recognition accuracy. The study aims to precisely identify the pavement transverse cracks by vehicle vibration response. A two-dimensional reconstruction method that transformed the vehicle vibration signal into an image was proposed. Five edge detection operators were used to generate the features of vibration reconstruction images. Besides, this study combined two basic characteristics, Euler number, and total target area. Eight classification models based on the above two features were trained by the RUSBoost integrated classifier. The results showed that the classification model based on the Prewitt operator eight connected domain Euler number and the total target area performed excellently. The testing accuracy can reach 93.5%. The classification model presented in this study has an excellent effect on pavement transverse crack identification and feasibility.

FPGA-Based Improved Sobel Operator Edge Detection

Image edge detection is an important field in image processing, with its primary goal being to identify edge information of various objects in images. Traditional edge detection programs are mostly executed serially, resulting in low detection efficiency. This paper proposes an FPGA-based improved Sobel operator edge detection method, harnessing the parallel processing capability of FPGA to enhance detection efficiency. Building upon the Sobel operator in edge detection algorithms, this method increases the convolution calculation dimensions of the Sobel operator, expanding it from 2 directions to 8 directions for more precise edge information. Images captured by OV5640 are processed within the FPGA and displayed via VGA. Compared to other edge detection algorithms, the FPGA-based improved Sobel operator edge detection method efficiently and rapidly detects image edges, ensuring stable image display and accurate edge information.

Numerical implementation of level set image recovery based on partial differential equation model

Abstract Image recovery is a class of ill-posed inverse problems, so image recovery has been a challenging and fundamental topic in image processing. In this paper, firstly, to recover blurred images with noise, an adaptive fully variable partial differential image recovery model is proposed based on the fully variable partial differential equation model and Gaussian thermal diffusion model, where the adaptiveness is achieved logarithmically by regularizing the edge detection operator. Secondly, a finite difference method, which covers the gradient and scatters operators in the partial differential equation, is applied to the problem of fixing the global range boundary conditions of the image. Finally, the gradient of the image is restored, and then the gradient domain of the image is solved to obtain the complete recovered image. The results show that in the evaluation of the image restoration effect aspect by two indexes, PSNR and SSIM, the value of the adaptive fully variable partial differential image restoration model increases from 28 to 32, and the value of SSIM increases from 0.70 to 0.88, which has good image restoration performance. The adaptive fully variable partial differential image restoration model proposed in this paper has the characteristics of good edge protection and fast convergence, which is of guiding significance for the numerical implementation of image restoration techniques.

Texture and pixel intensity characterization-based image segmentation with morphology and watershed techniques

<p>Image segmentation is an image processing technique that concentrated on finding and locating the parts of an image such as objects and boundaries. The purpose of locating these parts is for use in further processing analysis of an image such as recognition tasks, and content-based image retrieval. This paper introduces the segmentation procedure using a proposed template of features with watershed or morphology operations. Features template based on segmentation process conveys pixels’ intensities property perceived by the threshold value of histogram representation and texture feature where the regions are characterized by their texture content using standard deviation (SD) filtering. Wiener filter and histogram equalization (HE) techniques are used as preprocessing operations to enhance the image quality. The edge detector operator is hybridized to boost the segmentation process. Some statistical metrics are used for assessing and analyzing the performance of the stages in the proposed work. As a result, this proposed template of features achieved more performance with watershed and morphology segmentation.</p>

Research on physical human-computer interaction virtual reality fitness method combined with Unity3D technology

Abstract To respond to the slogan of the national fitness movement, improve the quality of the national fitness movement, and provide scientific reference to public fitness, this paper studies the fitness method of Unity3D technology and somatosensory human-computer interaction virtual reality technology. Firstly, we build a somatosensory HCI virtual fitness system with Unity3D and Kinect technology, verify the data with an edge detection operator, get the simulation results of the somatosensory HCI virtual reality fitness system, and then get the fitness method based on the somatosensory HCI virtual reality technology according to the results. The data show that the fitness industry is seeing new highs as the national fitness membership reaches 75.13 million people as of December 2021, while the growth of women among them increases from 13.5 million in 2017 to 55.6 million in 2021. Among the 800 fitness methods, the physical therapy and rehabilitation category accounts for 1/3, and fitness, bodybuilding and health care each account for about 1/5. The comfort zone of fitness differs between men and women, ranging from 0.6-1.2 for men and 0.5-0.9 for women. The research in this paper can provide reasonable fitness advice to the fitness population and promote the healthy development of the fitness industry.

Comparative Study and Analysis of Edge Detection Operators in Marker Controlled Watershed Transformation Algorithm on Various Medical Images

Comparative Study and Analysis of Edge Detection Operators in Marker Controlled Watershed Transformation Algorithm on Various Medical Images

Edge detection based on ghost imaging through biological tissue

Edge detection is an essential procedure in biomedical image processing. Note that several edge detection schemes based on ghost imaging (GI) have been proposed, while a practical application environment is not considered. In this paper, we first investigate the edge detection of target objects inside biological tissue in a GI system. The performances of four edge detection operators are compared in GI through biological tissue when refractive index turbulence is considered. The numerical results demonstrate that the Standard operator performs better in edge extraction regardless of the strength of refractive index fluctuations. Our results may provide potential applications of GI in the field of biomedical imaging.

Design of multilayer cellular neural network based on memristor crossbar and its application to edge detection

Memristor with memory properties can be applied to connection points (synapses) between cells in a cellular neural network (CNN). This paper highlights memristor crossbar-based multilayer CNN (MCM-CNN) and its application to edge detection. An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors. MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation. Figure of merit (FOM) is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results. Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.

Comparison of edge detection operators in digital image processing

The Internet has been developed so far, computer has developed rapidly, and the use of computer for image processing has been widely used in all walks of life, the use of MATLAB for image processing has become a hot topic today. Image processing can find the information it wants in many images repeatedly compressed on the network, and restore information to a certain extent. Edge processing step is included in various digital image processing models. So Edge processing is particularly important for image processing. Common edge detection algorithms include Prewitt, Sobel, Canny, and so on. Choosing a more appropriate and effective operator becomes particularly important for processing the image. The three operators are processed by grayscale, binarization, edge detection of different operators, and the final results are compared. Gaussian white noise is added to compare the white noise interference ability of the three operators. Finally, the Canny operator works better.

Detection of crack defects in carbon fiber composites using passive infrared thermography

Aiming at the problem of low detection accuracy in detection of damage location, This article proposes a method to reduce the local jitter in passive infrared thermal imaging by using wavelet hierarchic hard threshold denoising based on template convolution, and thresholds are defined using an unsupervised Kmeans classification algorithm, the traditional SAD template convolution matching algorithm and the Sobel operator edge detection are improved. The denoizing algorithm achieves improved the denoising strength, the smoothness, and effectively extracts the crack defect features. To increase the effectiveness of nondestructive examination, the rapid detection method of temperature data matching is utilized before the infrared detection experiment, and the damage position was marked on the normal sample to allow for intuitive assessment of the damage location. The results show that by matching temperature data, the damage degree of the specimen can be quickly determined, and that the damage features can be accurately extracted using improved image processing algorithm.

2D image edge detection enhancement using convolutional neural network

Traditional edge detection operators are usually applied on edge detection in 2D image processing. However, the edge detection system equipped with simple operators has many disadvantages, such as high sensitivity to noise and neglect of significant edge details. This work proposed a method to enhance edge detection with convolutional neural net-work. To overcome the shortcomings of the system using simple edge detection operators in 2D image processing, an edge detection system using convolutional neural network was developed with Python language. In the convolutional neural network, two convolutional layers were designed to extract 2D image features that were relative to edge information. Then a normalization layer was applied to normalize the convoluted output. After that, pre-processing was utilized to denoise and smooth the image input. The final step was edge detection using traditional operators. Experiments were also implemented to verify the improvement of the plugin of the three-layer convolutional neural network in the de-signed edge detection system. Relative frequencies were utilized to quantify the edge de-tection performance. Results showed that the involvement of convolutional neural net-work could strengthen edge detection operators performance obviously in computer vi-sion.

Multi-directional Sobel operator kernel on GPUs

Sobel is one of the most popular edge detection operators used in image processing. To date, most users utilize the two-directional 3×3 Sobel operator as detectors because of its low computational cost and reasonable performance. Simultaneously, many studies have been conducted on using large multi-directional Sobel operators to satisfy their needs considering the high stability, but at an expense of speed. This paper proposes a fast graphics processing unit (GPU) kernel for the four-directional 5×5 Sobel operator. To improve kernel performance, we implement the kernel based on warp-level primitives, which can significantly reduce the number of memory accesses. In addition, we introduce the prefetching mechanism and operator transformation into the kernel to significantly reduce the computational complexity and data transmission latency. Compared with the OpenCV-GPU library, our kernel shows high performances of 6.7x speedup on a Jetson AGX Xavier GPU and 13x on a GTX 1650Ti GPU.

A Self-Supervised Fault Detection for UAV Based on Unbalanced Flight Data Representation Learning and Wavelet Analysis

This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as the voltage of the generator, angle of attack, and position of the rudder surface. A UAV is a typical complex system, and its flight data are typical high-dimensional large sample data sets. In practical applications such as UAV fault detection, the fault data only appear in a small part of the data sets. In this study, representation learning is used to extract the normal features of the flight data and reduce the dimensions of the data. The normal data are used for the training of the Auto-Encoder, and the reconstruction loss is used as the criterion for fault detection. An Improved Auto-Encoder suitable for UAV Flight Data Sets is proposed in this paper. In the Auto-Encoder, we use wavelet analysis to extract the low-frequency signals with different frequencies from the flight data. The Auto-Encoder is used for the feature extraction and reconstruction of the low-frequency signals with different frequencies. To improve the effectiveness of the fault localization at inference, we develop a new fault factor location model, which is based on the reconstruction loss of the Auto-Encoder and edge detection operator. The UAV Flight Data Sets are used for hard-landing detection, and an average accuracy of 91.01% is obtained. Compared with other models, the results suggest that the developed Self-supervised Fault Detection Model for UAVs has better accuracy. Concluding this study, an explanation is provided concerning the proposed model’s good results.

Comparative Analysis of Image on Several Edge Detection Techniques

Edge detection of an image is needed to obtain information related to the size and shape of an image. There are numerous methods for detecting edges, including the Prewitt, Laplace, and Kirsch operators. Each edge detection method has different performance and results. Therefore, this study aims to analyze the performance comparison of the Prewitt, Laplace, and Kirsch operators. The analysis process is carried out using MSE, PSNR and Image Contrast values. Based on the experiments that have been carried out, the best edge detection is produced by the Prewitt operator. The average MSE and PSNR values obtained were 4.63 and 41.79 dB. The Laplace operator is good in the contrast value of 17.77. However, the contrast value only serves as a supporting parameter to clarify the differences in the results of each edge detection operator. So it can be concluded that the Prewitt edge detection method is the best method among the other two methods.

State Recognition of Multi-Nozzle Electrospinning Based on Image Processing

The online monitoring of a multi-jet electrospinning process is critical to the achievement of stable mass electrospinning for industrial applications. In this study, the construction of an ejection state recognition system of a multi-jet electrospinning process based on image processing is reported. The ejection behaviors regarding multi-nozzle electrospinning were recorded by CMOS industrial cameras in real time. The characteristic information regarding the multi-jet cone tip was obtained by processing the images regarding Roberts operator edge detection, Hough transform line detection, and mask histogram analysis. The jet anomalies of the hanging droplets in the nozzle outlet area could be obtained and identified by the vision. The identification accuracy towards the target hanging droplets was more than 85%. This work reports the intelligent control of large-scale multi-nozzle electrospinning equipment.

Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions

An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems.

A Novel Defect Segmentation for Pulse Infrared Images Based on Improved Fuzzy C-Means Algorithm With Weighted Distance

Pulsed infrared nondestructive testing (NDT) technology can effectively identify defects in engineering materials or structures. However, the original infrared images have the problems of low signal-to-noise ratio (SNR) and blurred edges. A novel defect segmentation method for pulsed infrared images based on improved fuzzy C-means algorithm with weighted distance is proposed. First, the original infrared images are denoised by wavelet transform. Second, an improved fuzzy C-means algorithm based on weighted distance (WDFCM) is proposed to cluster the infrared images. Afterward, the maximum between-cluster variance method (OTSU) is used to automatically select the thresholds and the binarization is performed to extract defect features. The Sobel edge detection operator is used to completely extract the structural properties of the defect regions. Finally, an experimental platform for pulsed infrared thermal imaging was built with two kinds of materials: flexible pipes and steel structures. The method proposed in this article was used for image segmentation subsequently. The results verify that the performance of the proposed method is optimal. The accuracy of defect detection can reach 98.95%, which is 16.82% higher than the original method.

Tapped area detection and new tapping line location for natural rubber trees based on improved mask region convolutional neural network.

Aiming at the problem that the rubber tapping robot finds it difficult to accurately detect the tapped area and locate the new tapping line for natural rubber trees due to the influence of the rubber plantation environment during the rubber tapping operation, this study proposes a method for detecting the tapped area and locating the new tapping line for natural rubber trees based on the improved mask region convolutional neural network (Mask RCNN). First, Mask RCNN was improved by fusing the attention mechanism into the ResNeXt, modifying the anchor box parameters, and adding a tiny fully connected layer branch into the mask branch to realize the detection and rough segmentation of the tapped area. Then, the fine segmentation of the existing tapping line was realized by combining edge detection and logic operation. Finally, the existing tapping line was moved down a certain distance along the center line direction of the left and right edge lines of the tapped area to obtain the new tapping line. The tapped area detection results of 560 test images showed that the detection accuracy, segmentation accuracy, detection average precision, segmentation average precision, and intersection over union values of the improved Mask RCNN were 98.23%, 99.52%, 99.6%, 99.78%, and 93.71%, respectively. Compared with other state-of-the-art approaches, the improved Mask RCNN had better detection and segmentation performance, which could better detect and segment the tapped area of natural rubber trees under different shooting conditions. The location results of 560 new tapping lines under different shooting conditions showed that the average location success rate of new tapping lines was 90% and the average location time was 0.189 s. The average values of the location errors in the x and y directions were 3 and 2.8 pixels, respectively, and the average value of the total location error was 4.5 pixels. This research not only provides a location method for the new tapping line for the rubber tapping robot but also provides theoretical support for the realization of rubber tapping mechanization and automation.

Automated Detection of Malaria and Sickle Cells in Blood Smears Using Edge Detection and Morphological Operations

Automated Detection of Malaria and Sickle Cells in Blood Smears Using Edge Detection and Morphological Operations

Multi-scale local feature enhanced transformer network for pavement crack detection

目的 道路裂缝是路面病害的早期征兆。定期监测路面状况、及时准确地发现路面裂缝对于交通养护机构降低成本、保证路面结构的可靠性和耐久性以及提高驾驶安全性、舒适性有重要意义。目前基于卷积神经网络的深度学习模型在长距离依赖建模方面存在不足，模型精度难以满足真实路面环境下的裂缝检测任务。一些模型通过引入空间/通道注意力机制进行长距离依赖建模，但是会导致计算量和计算复杂程度增加，无法实现实时检测。鉴于此，本文提出一种基于Transformer编码—解码结构的深度神经网络道路裂缝检测模型CTNet （crack transformernetwork）。方法 该模型主要由Transformer注意力模块、多尺度局部特征增强模块、上采样模块和跨越连接4部分构成。采用Transformer注意力机制能更有效提取全局和长距离依赖关系，克服传统卷积神经网络表征输入信息的短距离相关缺陷。同时，为适应裂缝尺寸变化多样性，将Transformer与多尺度局部特征增强模块相结合，从而有效整合不同尺度局部信息，克服Transformer局部特征建模不足。结果 通过与DeepCrack模型在不同裂缝检测数据集中的比较表明，本文提出的多尺度局部特征增强Transformer网络能快速、准确地分割路面裂缝，且效率更优。定量研究结果表明，CTNet在更有挑战性的CrackLS315数据集中的精度、召回率和F1值达到91.38%、80.38%和85.53%，明显优于对比方法。在CrackWH100数据集中，精度、召回率和F1值进一步提升，分别达到92.70%、90.52%和91.60%。此外，CTNet的训练速率提升至DeepCrack模型的6.78倍。结论 CTNet可以实现强噪声背景下的道路裂缝检测，检测效果优于目前最优方法，且参数量小，易于训练和部署。;Objective The pavement-relevant inspection is focused on the optimization for pavement cracks early-alarming detection and the preservation of pavement structure. However，conventional image processing-based techniques are laborintensive and time-consuming，such as edge detection，threshold segmentation，template matching，and morphology operations. It is challenged to the geometric and spectral complexities of pavement crack and its contexts（e. g. ，illumination variation，oil or water stains，and shadows caused by trees and vehicles）. The convolution neural network（CNN）based deep learning image processing techniques have been developing intensively. However，the CNN-based methods are less effective in long-range dependency modeling，which may cause insufficient detection results in complicated road surface scenarios. Some works are related to attention mechanisms like spatial or channel attention modules，and self-attention modules. However，these attention mechanism-based operations are still challenged for their sophistication and computational cost. Method To detect pavement cracks efficiently and effectively，we develop a novel Transformer-based encoderdecoder neural network，called CTNet，which consists of Transformer blocks，multi-scale local feature enhanced blocks， upsampling blocks，and skip connections. The CTNet can achieve more long-range dependency and global receptive field in terms of multi-head self-attention-based Transformer mechanism. Although Transformer is featured by high running efficiency and low computational overhead demand，it is infeasible to model local contextual information because Token generation can break the connections of neighboring regions. Thus，to capture more multi-scale local information，we design a multi-scale local feature-enhanced block in terms of a multiple dilation ratios-relevant dilation convolution block. Especially，the designed multi-scale local feature enhancement block is melted into each Transformer block for local information complement. Both of local and global low-level contextual features can be captured for feature enhancement. Afterwards，a novel decoder path is implemented to extract high-level features. The decoder consists of the Transformer blocks similar to the up-sampling blocks and the spatial details can be restored for end-to-end segmentation. Result To demonstrate the efficiency and effectiveness of our proposed CTNet，a series of comparative analyses and ablation studies are carried out on three datasets. First，the CTNet can optimize running efficiency，as well as comparable computation overhead and complexity compared to the current UNet，SegNet，DeepCrack，and SwinUnet. Second，CTNet is 6. 78 times faster than the second-best DeepCrack model in terms of training speed. On CrackLS315 dataset，quantitative analyses are also showed that the optimal CTNet is obtained a precision of 91. 38%，a recall of 80. 38%，and a F1 measure of 85. 53% of each；on CrackWH100 dataset，CTNet can obtain a precision of 92. 70%，a recall of 90. 52%，and a F1 measure 91. 60% of each as well. However，it is still challenged to lack of local information when pure-Transformer-based Swin-UNet performed not well compared to fully convolution networks. Furthermore，the CTNet is insufficient to converge when the local blocksenhanced are removed. In summary，the Transformer-based CTNet is beneficial to multi-scenario pavement cracks in terms of the global receptive field. The CTNet can get pavement crack detection results consistently. Conclusion The proposed CTNet has its potentials to deal with noisy pavement images for pavement crack detection.