Edge Detection Process Research Articles

Bio-inspired Design and Performance Evaluation of Lawnmower Cutter Blade

This study presents a bio-inspired design approach for the engineering applications of agricultural implements, with practical implications for the field. The CAD modeling of bio-inspired serrated lawnmower cutter blades was followed by structural and fatigue analysis with a comparative evaluation of the performance of conventional plain cutter blades. This method involved the bionic transformation of a biological model of the serrated leg of the grasshopper, traced using MATLAB edge detection and image processing software. The solid modeling CAD software for the Bionic Blade lawnmower cutter blade converts this biological model into an engineering model. After designing the geometric shape of the bionic tooth profile's serrated cutting edge, the boundary conditions are evaluated for numerical simulation based on the machine specifications of the conventional lawnmower cutter. After analyzing the power of the motor, the rotational speed, and the torque, the static structural and fatigue analysis was carried out in numerical simulation software to synthesize the performance of the bionic lawnmower cutter blade for engineering analysis of deformation, shear stress, and fatigue life. The bionic design of the blade and conventional plain blades were manufactured using an Amada CO2 laser cutting machine and sheet metal bending processes for Stainless Steel (SS 304) material. The experimental validation of software simulation was carried out by field trials using the Bionic design of a lawnmower cutter blade to evaluate the performance improvement in cutting time and yield of grass cut in kg for 60 to 100 meters distance cutting. After comparing the performance of the plain blade and the design of the bionic blade, there was a reduction in abrasive wear, a 12.87% improvement in the cutting time, and a 14.8% increase in the yield of grass compared to the conventional blade. This performance improvement was attributed to the unique bionic design of serrated cutting-edge and ground clearance. The bio-inspired design approach has immense practical applications in designing agricultural implements, as confirmed by this performance evaluation of this case study of bio-inspired lawnmower cutter blades.

Research on Weld Seam Marking Based on Machine Vision Technology

The rapid development of machine vision technology has made it widely used in various industrial fields. The detection of welding quality can also be achieved through this technology. The core of welding quality inspection is to realize the marking of weld features. To solve the problem of weld feature marking, the GUI weld marking interface is built based on MATLAB. After the process of image acquisition, image preprocessing, edge detection and feature extraction, the weld marking experiment of is finally completed. Based on data analysis of the experiment, it can be concluded that Canny edge detection operator has the best marking effect and the most stable detection for the research object used.

Beehive Smart Detector Device for the Detection of Critical Conditions That Utilize Edge Device Computations and Deep Learning Inferences.

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3-5% better accuracy results.

A Comprehensive Review of Edge Detection Methods

Boundaries are typified by edges, which pose a basic challenge in image processing. Images with edge detection have less data, less extraneous information filtered out, and key structural elements preserved. Finding the borders of objects or textures in a picture is the process of edge detection. The speed of image processing is a highly challenging problem since edge detection data is very huge. Various approaches are employed for edge detection, contingent on the needs of different applications. This study offers a thorough analysis of numerous studies and approaches used in the edge detection field.

Classification of Medicinal Plant Leaves for Types and Diseases with Hybrid Deep Learning Methods

Leaf images are often used to detect plant diseases because most disease symptoms appear on the leaves. Analyzes performed by experts in the laboratory environment are expensive and time consuming. Therefore, there is a need for automated plant disease detection systems that are both economical and can help diagnose early symptoms more accurately. In this study, a deep learning-based methodology is presented for the classification of leaf diseases of plants, which are very similar in color, texture, vein and shape and cannot be noticed by non-experts, which are important for traditional medicine and pharmaceutical industry. In the model development process, 7 pre-learning deep learning algorithms and an image data set created from plant leaves in 10 categories were preferred. The proposed model classifies the plant type and diseased condition in the dataset. In the first step of training the model, different learning rates were tested with optimum hyperparameters. In the second part, a test accuracy rate of 98.69% was achieved with the DenseNet121 model, with increased data. At the last stage, after the edge detection processes, the test accuracy value of 67.92% was reached with the DenseNet 121 model.

Tunable optical spatial differential operation via photonic spin Hall effect in a Weyl semimetal.

Optical differential operation is the basic principle of optical image edge detection, which has the advantages of high efficiency, simple structure and markerless compared with the traditional digital image processing methods. In this paper, we propose an optical differential operation with high contrast based on the photonic spin Hall effect in a Weyl semimetal, which enables to switch between one- and two-dimensional edge detection. Due to the unique optical and electrical properties of the Weyl semimetal, a transport model for the differential operation is established, which is closely related to the beam shifts. By tuning the incidence conditions, we effectively manipulate the in-plane and transverse shifts to switch differential operations between one and two dimensions. The contrast of the differential operation is further regulated by changing the physical parameters of the Weyl semimetal, and can be improved by two orders of magnitude compared to the conventional differentiator. This study provides new possibilities in edge detection and image processing owing to the advantages of switchable dimension and high contrast.

Monitoring climate change effects on coral reefs using edge-based image segmentation

Coral reefs are valuable ecosystems that face vulnerability to climate change impacts. Underwater images often encounter noise from various factors, such as water turbidity, lighting conditions, attenuation, and scattering, which can complicate edge detection and segmentation processes, leading to inaccuracies. However, image processing techniques offer a viable solution to this issue. In this study, an edge-based segmentation approach is proposed that uses multiple contrast techniques to detect and quantify changes in coral reef imagery. The proposed approach effectively identifies changes in coral reef imagery, making it a valuable tool for monitoring climate change's effects on these ecosystems. Furthermore, high-resolution images at different time points and locations were collected, and then an edge-based segmentation approach was utilized to enhance the accuracy of edge detection and segmentation. Comparing the proposed method with traditional segmentation techniques showed a significant improvement in terms of segmentation precision. Subsequently, alterations in the structure and composition of coral reefs are observed, indicating the influence of climate change on these ecosystems. This research highlights the capabilities of image processing techniques using edge-based segmentation in monitoring coral reefs. It offers an effective and precise approach to detecting changes in coral reef images, thereby contributing to conservation endeavors.

Brake disc positioning and defect detection method based on improved Canny operator

AbstractFirstly, the Canny operator combined with circle fitting is used to position the working surface. Secondly, the median filter and the Laplacian operator are selected for preprocessing. Due to uneven illumination, surface texture, or environmental dust, the extracted scratches exhibit fragmentation, and traditional morphological operations cannot handle scratches well, so an improved Canny operator combined with probabilistic Hough transform is proposed for scratch connection. A bidirectional connection is introduced to optimize the edge detection process of the Canny operator, and a parallel algorithm is introduced to shorten the detection time. Finally, the standard Hough transform needs to traverse the entire parameter space, and the calculation is complicated, and the probability Hough transform is selected. The experimental results show that compared with the traditional Canny algorithm, the precision, recall, and F1‐score of the improved Canny algorithm are increased by 3.2%, 13.2%, and 6.0%, respectively. After adding parallel processing, the average detection time was reduced by 45 ms. Finally, the accuracy of scratches extracted using this algorithm reached 97.96%, the leak rate, and mistake rate of this study are only 3.33% and 2.00%, which provides a more accurate and efficient theoretical support for the research on brake disc scratch detection.

Linear Diophantine Fuzzy Sets: Image Edge Detection Techniques based on Similarity Measures

In the digital imaging process, fuzzy logic provides many advantages, including uncertainty management, adaptability to variations, noise tolerance, and adaptive classification. One of the techniques of digital image processing is the edge detection. The edge detection process is an essential tool to segment the foreground objects from the image background. So, it facilitates subsequent analysis and comprehension of the image’s underlying structural properties. This process can be moved on with the notion of fuzzy sets and their generalizations. The concept of Linear Diophantine fuzzy sets is a generalization of fuzzy sets where reference parameters correspond to membership and non-membership grades. This study aims to apply linear Diophantine fuzzy sets (LDFSs) to edge detection of images. The novelty of this paper is twofold. The first one is that we conduct a comprehensive evaluation to ascertain the similarity values using the linear Diophantine fuzzy similarity measure by leveraging the gray normalized membership values associated with fundamental edge detection techniques. The other is to modify the image pixels into the LDFSs and then filter the images by using the presented similarity measure operators given in the LDFS environment.

Digital Image Processing On Kaffir Orange Peel With Canny Edge Detection Algorithm

Object tracking is a form of application of computer vision. To be able to track an object, a stage is needed in the image processing process. Image Processing is a field related to the process of image transformation (image). The image processing process is carried out to obtain better image quality. The harvesting system in kaffir lime is done manually, by choosing fruits whose skin color is green, and not yellowish. Due to the small and asymmetrical size and shape of kaffir lime, manual harvesting systems are still widely used to maintain the quality and quantity of the harvest. In addition, the manual harvesting system can also avoid damage to kaffir lime trees and obtain optimally ripe kaffir limes. Kaffir lime also has genders like humans, namely males and females. In male kaffir lime there is a circle that is more prominent in size underneath, while female kaffir lime has a flat shape. However, for consumption and medicinal purposes both male and female kaffir lime can be used without affecting the taste or quality of the fruit. With the image processing to determine the level of wrinkles on quality kaffir lime peel, kaffir lime will be selected which is usually used for herbal medicines. In this case, the Canny Edge Detection algorithm can be used to identify density edges in kaffir lime peels. Thus, the degree of wrinkles in kaffir lime peel can be calculated and measured to be more accurate. And can be separated quality or non-quality kaffir lime with the image of kaffir lime that has been seen through the image. The results obtained in designing and analyzing the quality of kaffir lime are clearer and more accurate with an image resolution value of 248 x 216 that the orange is included in the female kaffir lime type.The results tested that the right edge detection method in carrying out the edge detection process in the image of kaffir lime peel is the Canny Edge Detection Algorithm. By using the image on the Canny Edge Detection Algorithm, more dense and quality kaffir lime results are obtained so that it can be used for herbal medicine.

Klasifikasi Citra Penyakit Daun Cabai Menggunakan Algoritma Learning Vector Quantization

<table width="593" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="388"><p>The problem often occurs in chili leaves is organisms that interfere with chili plants which can reduce chili production. There are chili plant diseases that are difficult for farmers to recognize by using their eyes and without using tools. The purpose of this study was to produce a model capable of identifying chili leaf diseases based on leaf colour in order to make it easier for farmers to identify chili leaf diseases, especially Phytophthora, Anthracnose, and Cercospora diseases, using the Learning Vector Quantization (LVQ) classification algorithm. Data was collected in the form of digital images of 30 chili leaves which were processed by resizing and transforming RGB to HSV which then proceeded to Canny Edge detection process with the aim of getting patterns from images of chili leaves. The result of testing LVQ algorithm using a confusion matrix get an accuracy of 80%, the precision value of 80%, recall value of 82%, and f-1 score of 81%.</p><p> </p></td></tr></tbody></table>

Deep learning based graphical password authentication approach against shoulder-surfing attacks

The password used to authenticate users is vulnerable to shoulder-surfing assaults, in which attackers directly observe users and steal their passwords without using any other technical upkeep. The graphical password system is regarded as a likely backup plan to the alphanumeric password system. Additionally, for system privacy and security, a number of programs make considerable use of the graphical password-based authentication method. The user chooses the image for the authentication procedure when using a graphical password. Furthermore, graphical password approaches are more secure than text-based password methods. In this paper, the effective graphical password authentication model, named as Deep Residual Network based Graphical Password is introduced. Generally, the graphical password authentication process includes three phases, namely registration, login, and authentication. The secret pass image selection and challenge set generation process is employed in the two-step registration process. The challenge set generation is mainly carried out based on the generation of decoy and pass images by performing an edge detection process. In addition, edge detection is performed using the Deep Residual Network classifier. The developed Deep Residual Network based Graphical Password algorithm outperformance than other existing graphical password authentication methods in terms of Information Retention Rate and Password Diversity Score of 0.1716 and 0.1643, respectively.

Cobble edge detection algorithm based on digital image processing

Cobble size distribution (CSD) on the surface of a cobble bed is an important part of the river system, with important implications for river ecology and geomorphology. A digital image edge detection algorithm based on markers and detection lines is proposed to realize automatic statistics gathering of CSD. By making multiple detection lines outward from the markers, the cobble edge detection process of 2D images is transformed into a 1D space for analysis and processing. To correct the error detection points, an edge correction algorithm based on mutation point detection is designed. Simultaneously, a cobble edge reconstruction algorithm is proposed to restore covered cobble edges. The results of a verification experiment and comparative analysis indicate that the relative error of D50, which was estimated by the algorithm in our study, is 3% in the laboratory and 8% in the field. The proposed method is reliable for the estimation of CSD with grain size range of 5 to 50 cm. In conclusion, the proposed algorithm is suitable for river geomorphology research and physical habitat studies of cobble floodplains using an unmanned aerial vehicle for remote sensing photography.

Calibrating contact parameters of typical rotary tillage components cutting soil based on different simulation methods

This report analyzes the problem of complex soil movement patterns under the action of coupled forces, such as tension and shear, in agricultural processes and aims to improve the accuracy of contact parameters used in discrete element simulation studies of rototiller-soil interactions. This study focuses on the soil of Shihezi cotton field in the 8th division of Xinjiang and investigates the rotating tiller roller as a soil-touching component of tillage machinery. A combination of simulations and physical testing is used. We perform angle of repose tests and use edge detection, fitting, and other image processing methods to automatically, quickly, and accurately detect the soil accumulation and angle calibration of the contact parameters with soil particles. Additionally, soil slip tests are conducted to calibrate the contact parameters between the soil and the rotary blades. Optimization is achieved based on orthogonal simulations and the Box-Behnken response surface method using physically measured values as the target. A regression model of the stacking angle and rolling friction angle is established to determine the optimal combination of simulation contact parameters: between soil and soil, the recovery coefficient is 0.402, static friction coefficient is 0.621, and rolling friction coefficient is 0.078; between soil contact parts and soil, the recovery coefficient is 0.508, static friction coefficient is 0.401, and rolling friction coefficient is 0.2. Furthermore, the calibration parameters are selected as contact parameters for the discrete element simulation. By combining the above two simulation methods to analyze and compare the simulation process of cutting soil from rototiller roller parts to rototiller single blade parts, we obtained the changes in energy, cutting resistance, and soil particle movement at different depths of the soil cutting process. Finally, the average cutting resistance was used as an index for validation in the field tests. The measured value is 0.96 kN and the error of the discrete element simulation is 13%. This demonstrates the validity of the calibrated contact parameters and the accuracy of the simulation, which can provide a theoretical reference and technical support for the study of the interaction mechanisms between of tillage equipment parts and soil, as well as the design and optimization of these interactions in the future.

RECOGNITION OF REALTIME BASED PRIMITIVE GEOMETRY OBJECTS USING PERCEPTRON NETWORK

The purpose of this study is to analyze the perceptron model on pattern recognition of primitive geometric objects in real time based on video images. The samples used in this study were cubes, prisms, tubes and balls. The system was built using the Delphi 7 programming language with pre-processing stages system training includes the process of calculating matrix values from the original image, then proceed with the grayscale and edge detection processes using convolution with a kernel, namely the sobel operator and then the matrix results from the edge detection process are transformed using a perceptron network to obtain energy from the image of the object, then the resulting energy The transformation is stored in the database as a system test reference pattern recognition energy. Measurement of system performance evaluation in this study uses two parameters, namely detection rate and false positive rate. The recognition rate of primitive geometric objects using the perceptron network model in this study reaches 60.00% to 80.00%. The detection rate percentage shows that this model can be used as a supporting approach for the recognition of geometric objects in video.

Density based smart traffic control system using canny edge detection algorithm along with object detection

It is urgently necessary to combine current advancements to work on the cutting edge inrush hour jam the executives, as urban congestion is one of the world’s biggest concerns. Existing methodologies, for example, traffic police and traffic lights are neither fulfilling nor viable. Consequently, a traffic management system that utilizes sophisticated edge detection and digital image processing to measure vehicle density in real time is developed in this setting. Computerizedimage processing should be used to detect edges. To extract significant traffic data from CCTV images, the edge recognition method is required. The astute edge finder outperforms other processes in terms of accuracy, entropy, PSNR (peak signal to noise ratio), MSE (mean square error), and execution time. There are a number of possible edge recognition calculations. In terms of reaction time, vehicle the board, mechanization, dependability, and overall productivity, this framework performs significantly better than previous models. Utilizing a few model images of various traffic scenarios, appropriate schematics are also provided for a comprehensive approach that includes image collection, edge distinguishing evidence, and green sign classification. Also recommended is a system with object identification and priority for ambulances stuck in traffic.

Deteksi Tepi untuk Mengidentifikasi Kanker Ginjal Menggunakan Metode Sobel, Roberts dan Canny

Kidneys are organs that function to filter metabolic waste in the blood and dispose of them in the form of urine. One of kidney disease is kidney cancer. Kidney cancer occurs due to gene mutations in kidney cells that cause kidney cells to grow abnormally and uncontrollably. To take an image of kidney cancer using a Magnetic Resonance Imaging (MRI) tool. The purpose of this research is to identify and recognize the pattern object of kidney cancer in the MRI image. To identify kidney cancer images, it begins with collecting image data, image processing, image edge detection, image thinning, and identification processes. Edge detection is used to detect the boundaries of objects in the image. The method used in this research is the Canny and Sobel method. The number of images taken were 23 images of kidney cancer samples. The results of this research show that the Canny method gives better image results than the Sobel image results

Optimization Ground Glass Opacities (GGO) Detection Using Multipixel Interpolation Techniques

Ground Glass Opacities (GGO) are a picture of abnormal lung conditions characterized by white or gray areas. This picture of GGO in the lungs could previously be detected based on the results of medical examinations such as Computerized Tomography (CT scan) and Magnetic Resonance Imaging (MRI) images of patients suffering from Covid-19. However, from the results of the examination, it can be seen that the CT scan and MRI images still have a noise level that is too high, causing difficulties in describing the distribution pattern of the GGO itself. The purpose of this study was to optimize the detection of GGO on MRI images using the Multipixel Interpolation technique. The detection process adopts several stages including image preprocessing, edge detection process, and gradient morphological segmentation. Image preprocessing is done to remove noise and improve the MRI input image. The edge detection process is carried out to detect lung organs automatically using the Canny method which is optimized with the multipixel interpolation technique. The final stage of the research is the segmentation process using a gradient morphology technique to see the spread of GGO in patients with Covid-19 contained in the MRI image. The results of this study present an overview of the GGO pattern with fairly good results. The results of the GGO pattern description will also measure the level of spread to see the severity of pneumonia. Based on the results presented, this research is useful as an alternative solution in the process of diagnosis and treatment of Covid-19 patients.

Highly Sensitive Mutual-Capacitive Fingerprint Sensor With Reference Electrode

The sensitivity of existing fingerprint sensors (FPSs) can decrease considerably owing to environmental factors and parasitic capacitance. In order to overcome this limitation, this paper proposes a highly-sensitive 300 dpi mutual-capacitive transparent fingerprint sensor (FPS) with uniquely designed reference lines for device security. Specifically, the reference lines of the FPS induce capacitance cancellation. Images of fingertips under dry, wet, and oily surface conditions were obtained in the presence and absence of the reference lines. The results showed that the fingerprints were significantly distorted in anomalous surface environments when the reference lines were not used. However, when the reference lines were used, the sensitivity improved irrespective of the environmental conditions. With the edge-detection processing, the proposed FPS exhibited 9.25 %, 61.49 %, and 8.60 % increase in the ridge sensing improvement (RSI) of dry, oil, and wet condition, respectively, thus significantly enhancing the sensing capability. Therefore, we believe the proposed FPS can increase device security owing to its excellent performance.

Semantic segmentation of landcover for cropland mapping and area estimation using Machine Learning techniques

ABSTRACT The paper has focussed on the global landcover for the identification of cropland areas. Population growth and rapid industrialization are somehow disturbing the agricultural lands and eventually the food production needed for human survival. Appropriate agricultural land monitoring requires proper management of land resources. The paper has proposed a method for cropland mapping by semantic segmentation of landcover to identify the cropland boundaries and estimate the cropland areas using machine learning techniques. The process has initially applied various filters to identify the features responsible for detecting the land boundaries through the edge detection process. The images are masked or annotated to produce the ground truth for the label identification of croplands, rivers, buildings, and backgrounds. The selected features are transferred to a machine learning model for the semantic segmentation process. The methodology has applied Random Forest, which has compared to two other techniques, Support Vector Machine and Multilayer perceptron, for the semantic segmentation process. Our dataset is composed of satellite images collected from the QGIS application. The paper has derived the conclusion that Random forest has given the best result for segmenting the image into different regions with 99% training accuracy and 90% test accuracy. The results are cross-validated by computing the Mean IoU and kappa coefficient that shows 93% and 69% score value respectively for Random Forest, found maximum among all. The paper has also calculated the area covered under the different segmented regions. Overall, Random Forest has produced promising results for semantic segmentation of landcover for cropland mapping.