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Journal articles on the topic 'EDGE DETECTION MODELS'

Author: Grafiati

Published: 11 September 2023

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1

Eom, K. B., and R. L. Kashyap. "Composite edge detection with random field models." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 1 (1990): 81–93. http://dx.doi.org/10.1109/21.47811.

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2

Luo, Shan, and Zehua Chen. "Edge detection in sparse Gaussian graphical models." Computational Statistics & Data Analysis 70 (February 2014): 138–52. http://dx.doi.org/10.1016/j.csda.2013.09.002.

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3

Yang, Chang Niu, and Xing Bo Sun. "Research on Jumper and Connector Detection of Silk Products." Applied Mechanics and Materials 716-717 (December 2014): 851–53. http://dx.doi.org/10.4028/www.scientific.net/amm.716-717.851.

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An improved morphological edge detection algorithm for silk products jumpers and connectors’ test was proposed. With structure elements of different models, we detect the edge information in different directions of silk products respectively; using the proposed adaptive fusion method based on histogram matching, we can obtain ideal image edge, while enhance the blurred edges, and effectively eliminate the silk products inherent texture and noise, then detect the clear jumpers and connectors.

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4

Gong, Rong Fen, and Mao Xiang Chu. "An Edge Detection Method Based on Adaptive Differential Operator." Applied Mechanics and Materials 713-715 (January 2015): 415–19. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.415.

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An edge detection method based on adaptive differential operator is proposed in this paper. Firstly, standard local edge models are built. And these edge models are described with four-bit-binary code (FBBC) which is obtained from weighted mean values in four directions. Then, based on weighted gray values in four directions, different differential operator templates are defined. And FBBC is used to build the matching between differential operator templates and edge models. Experiments show that this edge detection method with adaptive differential operator can smooth noise and has satisfactor

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Daoud, Mohammad I., Aamer Al-Ali, Rami Alazrai, et al. "An Edge-Based Selection Method for Improving Regions-of-Interest Localizations Obtained Using Multiple Deep Learning Object-Detection Models in Breast Ultrasound Images." Sensors 22, no. 18 (2022): 6721. http://dx.doi.org/10.3390/s22186721.

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Computer-aided diagnosis (CAD) systems can be used to process breast ultrasound (BUS) images with the goal of enhancing the capability of diagnosing breast cancer. Many CAD systems operate by analyzing the region-of-interest (ROI) that contains the tumor in the BUS image using conventional texture-based classification models and deep learning-based classification models. Hence, the development of these systems requires automatic methods to localize the ROI that contains the tumor in the BUS image. Deep learning object-detection models can be used to localize the ROI that contains the tumor, bu

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Ledalla, Sukanya, Vijendar Reddy Gurram, Gopala Krishna P, Saiteja Vodnala, Maroof Md, and Raviteja Reddy Annapuredddy. "Density based smart traffic control system using canny edge detection algorithm along with object detection." E3S Web of Conferences 391 (2023): 01061. http://dx.doi.org/10.1051/e3sconf/202339101061.

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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 recogniti

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De Borba, Anderson A., Arnab Muhuri, Mauricio Marengoni, and Alejandro C. Frery. "Feature Selection for Edge Detection in PolSAR Images." Remote Sensing 15, no. 9 (2023): 2479. http://dx.doi.org/10.3390/rs15092479.

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Edge detection is one of the most critical operations for moving from data to information. Finding edges between objects is relevant for image understanding, classification, segmentation, and change detection, among other applications. The Gambini Algorithm is a good choice for finding evidence of edges. It finds the point at which a function of the difference of properties is maximized. This algorithm is very general and accepts many types of objective functions. We use an objective function built with likelihoods. Imaging with active microwave sensors has a revolutionary role in remote sensi

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Pitas, I. "Markovian image models for image labeling and edge detection." Signal Processing 15, no. 4 (1988): 365–74. http://dx.doi.org/10.1016/0165-1684(88)90057-6.

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9

Naraghi, Mahdi Ghasemi. "Satellite images edge detection based on morphology models fusion." Indian Journal of Science and Technology 5, no. 7 (2012): 1–4. http://dx.doi.org/10.17485/ijst/2012/v5i7.5.

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Ahmed, Awa, and Osman Sharif. "Image Processing Techniques-based fire detection." Sulaimani Journal for Engineering Sciences 8, no. 1 (2021): 23–34. http://dx.doi.org/10.17656/sjes.10145.

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In this paper different fire detection systems and techniques has been reviewed, many techniques have been developed for the purpose of early fire detection in different scenarios. The most accurate technique used among all these methods is Image Processing based Techniques. Different color models like RGB, HSI, CIE L*a*b and YCbCr have been used along with different edge detection algorithms like Sobel and Novel edge detection, finally the color segmentation technique was discussed in the review paper. All the mentioned methods in these papers have significantly proved to detect fire and flam

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11

Li, Junqing, and Jiongyao Ye. "Edge-YOLO: Lightweight Infrared Object Detection Method Deployed on Edge Devices." Applied Sciences 13, no. 7 (2023): 4402. http://dx.doi.org/10.3390/app13074402.

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Existing target detection algorithms for infrared road scenes are often computationally intensive and require large models, which makes them unsuitable for deployment on edge devices. In this paper, we propose a lightweight infrared target detection method, called Edge-YOLO, to address these challenges. Our approach replaces the backbone network of the YOLOv5m model with a lightweight ShuffleBlock and a strip depthwise convolutional attention module. We also applied CAU-Lite as the up-sampling operator and EX-IoU as the bounding box loss function. Our experiments demonstrate that, compared wit

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12

Waili, Abdul Rasul AL. "Using Convolutional Neural Networks for Edge Detection in Medical Images to Determine Surgery Instrument Tools." June-July 2023, no. 34 (May 25, 2023): 13–25. http://dx.doi.org/10.55529/jaimlnn.34.13.25.

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Edge detection plays a crucial role in medical image analysis, particularly in surgical settings where accurate identification of surgical instrument tools is essential. In this paper, we explore the use of Convolutional Neural Networks (CNNs) for edge detection in medical images to determine surgical instrument tools. We present a comprehensive study that includes dataset selection, preprocessing techniques, network architecture design, training procedures, evaluation metrics, and experimental results. The CNN models were trained on a diverse dataset of medical images with annotated ground tr

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13

Liang, Yan Bing, Xiao Li Meng, and Shu Jiang An. "Canny Edge Detection Method and its Application." Applied Mechanics and Materials 50-51 (February 2011): 483–87. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.483.

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Digital Cameras positioning has a wide range of application in the aspect of traffic monitoring (e-police).In this paper, the author builds and solves the mathematical model of positioning of monocular by edge detection methods and physical principles of optical imaging of Gauss, and offers a distortion error algorithm to test models, and finally sets up to solve the problem of relative position of multi-cameras. The introduction of distortion error algorithm, could be used to quantitatively examine the models in the first two steps. In accordance with the image situation of multi-image planes

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14

Cazorla, M., F. Escolano, D. Gallardo, and R. Rizo. "Junction detection and grouping with probabilistic edge models and Bayesian." Pattern Recognition 35, no. 9 (2002): 1869–81. http://dx.doi.org/10.1016/s0031-3203(01)00150-9.

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15

Cakic, Stevan, Tomo Popovic, Srdjan Krco, Daliborka Nedic, Dejan Babic, and Ivan Jovovic. "Developing Edge AI Computer Vision for Smart Poultry Farms Using Deep Learning and HPC." Sensors 23, no. 6 (2023): 3002. http://dx.doi.org/10.3390/s23063002.

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This research describes the use of high-performance computing (HPC) and deep learning to create prediction models that could be deployed on edge AI devices equipped with camera and installed in poultry farms. The main idea is to leverage an existing IoT farming platform and use HPC offline to run deep learning to train the models for object detection and object segmentation, where the objects are chickens in images taken on farm. The models can be ported from HPC to edge AI devices to create a new type of computer vision kit to enhance the existing digital poultry farm platform. Such new senso

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Yin, Zhenyu, Zisong Wang, Chao Fan, Xiaohui Wang, and Tong Qiu. "Edge Detection via Fusion Difference Convolution." Sensors 23, no. 15 (2023): 6883. http://dx.doi.org/10.3390/s23156883.

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Edge detection is a crucial step in many computer vision tasks, and in recent years, models based on deep convolutional neural networks (CNNs) have achieved human-level performance in edge detection. However, we have observed that CNN-based methods rely on pre-trained backbone networks and generate edge images with unwanted background details. We propose four new fusion difference convolution (FDC) structures that integrate traditional gradient operators into modern CNNs. At the same time, we have also added a channel spatial attention module (CSAM) and an up-sampling module (US). These struct

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17

Yadav, Dhirendra Prasad, Ashish Sharma, Senthil Athithan, Abhishek Bhola, Bhisham Sharma, and Imed Ben Dhaou. "Hybrid SFNet Model for Bone Fracture Detection and Classification Using ML/DL." Sensors 22, no. 15 (2022): 5823. http://dx.doi.org/10.3390/s22155823.

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An expert performs bone fracture diagnosis using an X-ray image manually, which is a time-consuming process. The development of machine learning (ML), as well as deep learning (DL), has set a new path in medical image diagnosis. In this study, we proposed a novel multi-scale feature fusion of a convolution neural network (CNN) and an improved canny edge algorithm that segregate fracture and healthy bone image. The hybrid scale fracture network (SFNet) is a novel two-scale sequential DL model. This model is highly efficient for bone fracture diagnosis and takes less computation time compared to

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18

Sadjadi, Ebrahim Navid, Danial Sadrian Zadeh, Behzad Moshiri, Jesús García Herrero, Jose Manuel Molina López, and Roemi Fernández. "Application of Smooth Fuzzy Model in Image Denoising and Edge Detection." Mathematics 10, no. 14 (2022): 2421. http://dx.doi.org/10.3390/math10142421.

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In this paper, the bounded variation property of fuzzy models with smooth compositions have been studied, and they have been compared with the standard fuzzy composition (e.g., min–max). Moreover, the contribution of the bounded variation of the smooth fuzzy model for the noise removal and edge preservation of the digital images has been investigated. Different simulations on the test images have been employed to verify the results. The performance index related to the detected edges of the smooth fuzzy models in the presence of both Gaussian and Impulse (also known as salt-and-pepper noise) n

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Bathiany, Sebastian, Johan Hidding, and Marten Scheffer. "Edge Detection Reveals Abrupt and Extreme Climate Events." Journal of Climate 33, no. 15 (2020): 6399–421. http://dx.doi.org/10.1175/jcli-d-19-0449.1.

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AbstractThe most discernible and devastating impacts of climate change are caused by events with temporary extreme conditions (“extreme events”) or abrupt shifts to a new persistent climate state (“tipping points”). The rapidly growing amount of data from models and observations poses the challenge to reliably detect where, when, why, and how these events occur. This situation calls for data-mining approaches that can detect and diagnose events in an automatic and reproducible way. Here, we apply a new strategy to this task by generalizing the classical machine-vision problem of detecting edge

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20

Prochazkova, Jana, David Procházka, and Jaromír Landa. "Sharp Feature Detection as a Useful Tool in Smart Manufacturing." ISPRS International Journal of Geo-Information 9, no. 7 (2020): 422. http://dx.doi.org/10.3390/ijgi9070422.

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Industry 4.0 comprises a wide spectrum of developmental processes within the management of manufacturing and chain production. Presently, there is a huge effort to automate manufacturing and have automatic control of the production. This intention leads to the increased need for high-quality methods for digitization and object reconstruction, especially in the area of reverse engineering. Commonly used scanning software based on well-known algorithms can correctly process smooth objects. Nevertheless, they are usually not applicable for complex-shaped models with sharp features. The number of

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21

Horrocks, Tom, Eun-Jung Holden, Daniel Wedge, and Chris Wijns. "A nonparametric boundary detection technique applied to 3D inverted surveys of the Kevitsa Ni-Cu-PGE deposit." GEOPHYSICS 83, no. 1 (2018): IM1—IM13. http://dx.doi.org/10.1190/geo2017-0085.1.

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Geophysical inversion can produce 3D models of the subsurface’s physical properties. The smoothness of property variations in these models makes it challenging to automatically find boundaries of homogeneous regions, where these boundaries may have implications for petrophysical transition and are significant for geologic interpretation. We have developed a new boundary detection technique that nonparametrically identifies and subtracts homogeneous regions from the 3D model, leaving exposed edges. The method is based on kernel density estimation of local property variations, in which the numbe

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22

Zhou, Liping, Wei-Bang Chen, and Chengcui Zhang. "Authorship Detection and Encoding for eBay Images." International Journal of Multimedia Data Engineering and Management 2, no. 1 (2011): 22–37. http://dx.doi.org/10.4018/jmdem.2011010102.

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This paper describes a framework to detect authorship of eBay images. It contains three modules: editing style summarization, classification and multi-account linking detection. For editing style summarization, three approaches, namely the edge-based approach, the color-based approach, and the color probability approach, are proposed to encode the common patterns inside a group of images with similar editing styles into common edge or color models. Prior to the summarization step, an edge-based clustering algorithm is developed. Corresponding to the three summarization approaches, three classi

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23

Liu, Jing, and Yimin Shao. "An improved analytical model for a lubricated roller bearing including a localized defect with different edge shapes." Journal of Vibration and Control 24, no. 17 (2017): 3894–907. http://dx.doi.org/10.1177/1077546317716315.

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Vibrations of a roller bearing (RB) with a localized defect (LOD) are determined by LOD edge shapes, which can be used to detect and diagnose the LODs. Therefore, it is very helpful to analyze the relationships between impulses and LOD edge shapes for detection and diagnosis of the early LODs. In this study, an improved analytical model for a lubricated RB with a LOD considering different edge shapes is proposed. The LOD edge propagation is determined by the size of small cylindrical surface at its edge. A time-varying impact force (TVIF) model for the LOD with different edge shapes is also pr

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24

Hou, Shuai, Jizhe Lu, Enguo Zhu, Hailong Zhang, and Aliaosha Ye. "A Federated Learning-Based Fault Detection Algorithm for Power Terminals." Mathematical Problems in Engineering 2022 (July 19, 2022): 1–10. http://dx.doi.org/10.1155/2022/9031701.

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Power terminal is an important part of the power grid, and fault detection of power terminals is essential for the safety of the power grid. Existing fault detection of power terminals is usually based on artificial intelligent or deep learning models in the cloud or edge servers to achieve high accuracy and low latency. However, these methods cannot protect the privacy of the terminals and update the detection model incrementally. A terminal-edge-server collaborative fault detection model based on federated learning is proposed in this study to improve the accuracy of fault detection, reduce

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25

Leeds, Daniel, and Michael Tarr. "Mixing hierarchical edge detection and medial axis models of object perception." Journal of Vision 15, no. 12 (2015): 1095. http://dx.doi.org/10.1167/15.12.1095.

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26

Baştan, Muhammet, Syed Saqib Bukhari, and Thomas Breuel. "Active Canny: edge detection and recovery with open active contour models." IET Image Processing 11, no. 12 (2017): 1325–32. http://dx.doi.org/10.1049/iet-ipr.2017.0336.

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27

Gerling, Gregory J., and Geb W. Thomas. "Two Dimensional Finite Element Modeling to Identify Physiological Bases for Tactile Gap Discrimination." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 10 (2005): 891–95. http://dx.doi.org/10.1177/154193120504901004.

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Tactile edge and gap detection are fundamental to performing manual tasks. Because slowly adapting type I (SA-I) mechanoreceptors encode details pertinent to edge localization, understanding low-level encoding is critical to understanding edge perception. Solid mechanics models may help us understand how mechanoreceptors in the skin encode applied surface indentation into neural signals representing edges. Finite element models test whether an indenter separated by a gap creates unique stress/strain distributions in models based upon orientation to fingerprint lines. Results indicate that a ga

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28

Dinh, Duc-Liem, Hong-Nam Nguyen, Huy-Tan Thai, and Kim-Hung Le. "Towards AI-Based Traffic Counting System with Edge Computing." Journal of Advanced Transportation 2021 (June 27, 2021): 1–15. http://dx.doi.org/10.1155/2021/5551976.

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The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detecti

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Fan, Lili, Jiabin Yuan, Keke Zha, and Xunan Wang. "ELCD: Efficient Lunar Crater Detection Based on Attention Mechanisms and Multiscale Feature Fusion Networks from Digital Elevation Models." Remote Sensing 14, no. 20 (2022): 5225. http://dx.doi.org/10.3390/rs14205225.

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The detection and counting of lunar impact craters are crucial for the selection of detector landing sites and the estimation of the age of the Moon. However, traditional crater detection methods are based on machine learning and image processing technologies. These are inefficient for situations with different distributions, overlaps, and crater sizes, and most of them mainly focus on the accuracy of detection and ignore the efficiency. In this paper, we propose an efficient lunar crater detection (ELCD) algorithm based on a novel crater edge segmentation network (AFNet) to detect lunar crate

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Shi, Xuetao, Hongli Zhao, Hailin Jiang, Huijun Zuo, and Qiang Zhang. "Edge Intelligence-Based OCS Fault Detection in Rail Transit Systems." Wireless Communications and Mobile Computing 2023 (April 11, 2023): 1–11. http://dx.doi.org/10.1155/2023/8659679.

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The Overhead Contact System (OCS) is critical infrastructure for train power supply in rail transit systems. OCS state monitoring and fault detection are indispensable to guarantee the safety of railway operations. The existing human-based OCS state monitoring and fault diagnosing method has some inherent drawbacks, such as poor real-time capability, low detecting precision, and waste of human resources. Edge Intelligence (EI) can perform complex computing tasks offloaded from trains within a little delay, and it is believed to help empower the OCS. In this paper, we propose an EI-based OCS st

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31

Fishbach, Alon, Israel Nelken, and Yehezkel Yeshurun. "Auditory Edge Detection: A Neural Model for Physiological and Psychoacoustical Responses to Amplitude Transients." Journal of Neurophysiology 85, no. 6 (2001): 2303–23. http://dx.doi.org/10.1152/jn.2001.85.6.2303.

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Primary segmentation of visual scenes is based on spatiotemporal edges that are presumably detected by neurons throughout the visual system. In contrast, the way in which the auditory system decomposes complex auditory scenes is substantially less clear. There is diverse physiological and psychophysical evidence for the sensitivity of the auditory system to amplitude transients, which can be considered as a partial analogue to visual spatiotemporal edges. However, there is currently no theoretical framework in which these phenomena can be associated or related to the perceptual task of auditor

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32

Wang, Dongcheng, Yanghuan Xu, Bowei Duan, et al. "Intelligent Recognition Model of Hot Rolling Strip Edge Defects Based on Deep Learning." Metals 11, no. 2 (2021): 223. http://dx.doi.org/10.3390/met11020223.

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The edge of a hot rolling strip corresponds to the area where surface defects often occur. The morphologies of several common edge defects are similar to one another, thereby leading to easy error detection. To improve the detection accuracy of edge defects, the authors of this paper first classified the common edge defects and then made a dataset of edge defect images on this basis. Subsequently, edge defect recognition models were established on the basis of LeNet-5, AlexNet, and VggNet-16 by using a convolutional neural network as the core. Through multiple groups of training and recognitio

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33

LYON, DOUGLAS A. "ON THE USE OF A VISUAL CORTICAL SUB-BAND MODEL FOR INTERACTIVE HEURISTIC EDGE DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 04 (2004): 583–606. http://dx.doi.org/10.1142/s0218001404003381.

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We present a novel interactive edge detection algorithm that combines A* search with low-level adaptive image processing. The algorithm models the semantically driven interpretation that we hypothesize to occur between the mind and visual cortex in the human brain. The basic idea is that oriented Gabor sub-bands are used to model grating cells in the mammalian visual system. These sub-bands are used during the search for a path to a marker in an image. A domain expert uses image markers to select edges of interest.We demonstrate the system in several image domains. Examples are shown in the ar

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34

Seo, Jihyun, Sumin Jang, Jaegeun Cha, Hyunhwa Choi, Daewon Kim, and Sunwook Kim. "MDED-Framework: A Distributed Microservice Deep-Learning Framework for Object Detection in Edge Computing." Sensors 23, no. 10 (2023): 4712. http://dx.doi.org/10.3390/s23104712.

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The demand for deep learning frameworks capable of running in edge computing environments is rapidly increasing due to the exponential growth of data volume and the need for real-time processing. However, edge computing environments often have limited resources, necessitating the distribution of deep learning models. Distributing deep learning models can be challenging as it requires specifying the resource type for each process and ensuring that the models are lightweight without performance degradation. To address this issue, we propose the Microservice Deep-learning Edge Detection (MDED) fr

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35

Pandey, Amit, Aman Gupta, and Radhey Shyam. "FACIAL EMOTION DETECTION AND RECOGNITION." International Journal of Engineering Applied Sciences and Technology 7, no. 1 (2022): 176–79. http://dx.doi.org/10.33564/ijeast.2022.v07i01.027.

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Facial emotional expression is a part of face recognition, it has always been an easy task for humans, but achieving the same with a computer algorithm is challenging. With the recent and continuous advancements in computer vision and machine learning, it is possible to detect emotions in images, videos, etc. A face expression recognition method based on the Deep Neural Networks especially the convolutional neural network (CNN) and an image edge detection is proposed. The edge of each layer of the image is retrieved in the convolution process after the facial expression image is normalized. To

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Kim, Youngpil, Shinuk Yi, Hyunho Ahn, and Cheol-Ho Hong. "Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment." Sensors 23, no. 2 (2023): 858. http://dx.doi.org/10.3390/s23020858.

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Defects or cracks in roads, building walls, floors, and product surfaces can degrade the completeness of the product and become an impediment to quality control. Machine learning can be a solution for detecting defects effectively without human experts; however, the low-power computing device cannot afford that. In this paper, we suggest a crack detection system accelerated by edge computing. Our system consists of two: Rsef and Rsef-Edge. Rsef is a real-time segmentation method based on effective feature extraction that can perform crack image segmentation by optimizing conventional deep lear

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Liao, Xiaolian, Jun Li, Leyi Li, Caoxi Shangguan, and Shaoyan Huang. "RGBD Salient Object Detection, Based on Specific Object Imaging." Sensors 22, no. 22 (2022): 8973. http://dx.doi.org/10.3390/s22228973.

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RGBD salient object detection, based on the convolutional neural network, has achieved rapid development in recent years. However, existing models often focus on detecting salient object edges, instead of objects. Importantly, detecting objects can more intuitively display the complete information of the detection target. To take care of this issue, we propose a RGBD salient object detection method, based on specific object imaging, which can quickly capture and process important information on object features, and effectively screen out the salient objects in the scene. The screened target ob

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Freitas, Nuno, Daniel Silva, Carlos Mavioso, Maria J. Cardoso, and Jaime S. Cardoso. "Deep Edge Detection Methods for the Automatic Calculation of the Breast Contour." Bioengineering 10, no. 4 (2023): 401. http://dx.doi.org/10.3390/bioengineering10040401.

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Breast cancer conservative treatment (BCCT) is a form of treatment commonly used for patients with early breast cancer. This procedure consists of removing the cancer and a small margin of surrounding tissue, while leaving the healthy tissue intact. In recent years, this procedure has become increasingly common due to identical survival rates and better cosmetic outcomes than other alternatives. Although significant research has been conducted on BCCT, there is no gold-standard for evaluating the aesthetic results of the treatment. Recent works have proposed the automatic classification of cos

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BOTHOREL, CECILE, JUAN DAVID CRUZ, MATTEO MAGNANI, and BARBORA MICENKOVÁ. "Clustering attributed graphs: Models, measures and methods." Network Science 3, no. 3 (2015): 408–44. http://dx.doi.org/10.1017/nws.2015.9.

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AbstractClustering a graph, i.e., assigning its nodes to groups, is an important operation whose best known application is the discovery of communities in social networks. Graph clustering and community detection have traditionally focused on graphs without attributes, with the notable exception of edge weights. However, these models only provide a partial representation of real social systems, that are thus often described using node attributes, representing features of the actors, and edge attributes, representing different kinds of relationships among them. We refer to these models asattrib

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Golding, Vaughn Peter, Zahra Gharineiat, Hafiz Suliman Munawar, and Fahim Ullah. "Crack Detection in Concrete Structures Using Deep Learning." Sustainability 14, no. 13 (2022): 8117. http://dx.doi.org/10.3390/su14138117.

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Infrastructure, such as buildings, bridges, pavement, etc., needs to be examined periodically to maintain its reliability and structural health. Visual signs of cracks and depressions indicate stress and wear and tear over time, leading to failure/collapse if these cracks are located at critical locations, such as in load-bearing joints. Manual inspection is carried out by experienced inspectors who require long inspection times and rely on their empirical and subjective knowledge. This lengthy process results in delays that further compromise the infrastructure’s structural integrity. To addr

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41

Berwo, Michael Abebe, Zhipeng Wang, Yong Fang, Jabar Mahmood, and Nan Yang. "Off-road Quad-Bike Detection Using CNN Models." Journal of Physics: Conference Series 2356, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2356/1/012026.

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Off-road vehicles are rapidly being employed for transportation, military activities, and sports racing. However, in monitoring and maintaining the race’s safety and reliability, quad-bike detection receives less attention than on-road vehicle recognition utilizing DL approaches. In this paper, we used transfer-learning approaches on pre-trained models of cutting-edge architectures, notably Yolov4, Yolov4-tiny, and Yolov5s, to detect quad-bikes from images and videos. A quad-bike dataset acquired from YouTube (https://youtu.be/ZyE3t3lG-vU. Accessed on April 10, 2022) was used to train and asse

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Koay, Hong Vin, Joon Huang Chuah, Chee-Onn Chow, Yang-Lang Chang, and Keh Kok Yong. "YOLO-RTUAV: Towards Real-Time Vehicle Detection through Aerial Images with Low-Cost Edge Devices." Remote Sensing 13, no. 21 (2021): 4196. http://dx.doi.org/10.3390/rs13214196.

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Object detection in aerial images has been an active research area thanks to the vast availability of unmanned aerial vehicles (UAVs). Along with the increase of computational power, deep learning algorithms are commonly used for object detection tasks. However, aerial images have large variations, and the object sizes are usually small, rendering lower detection accuracy. Besides, real-time inferencing on low-cost edge devices remains an open-ended question. In this work, we explored the usage of state-of-the-art deep learning object detection on low-cost edge hardware. We propose YOLO-RTUAV,

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Aw, Y. K., Robyn Owens, and John Ross. "An analysis of local energy and phase congruency models in visual feature detection." Journal of the Australian Mathematical Society. Series B. Applied Mathematics 40, no. 1 (1998): 97–122. http://dx.doi.org/10.1017/s0334270000012406.

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AbstractA variety of approaches have been developed for the detection of features such as edges, lines, and corners in images. Many techniques presuppose the feature type, such as a step edge, and use the differential properties of the luminance function to detect the location of such features. The local energy model provides an alternative approach, detecting a variety of feature types in a single pass by analysing order in the phase components of the Fourier transform of the image. The local energy model is usually implemented by calculating the envelope of the analytic signal associated wit

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Meyer, T., A. Brunn, and U. Stilla. "ACCURACY INVESTIGATION ON IMAGE-BASED CHANGE DETECTION FOR BIM COMPLIANT INDOOR MODELS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2021 (June 17, 2021): 105–12. http://dx.doi.org/10.5194/isprs-annals-v-4-2021-105-2021.

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Abstract. Construction progress documentation is currently of great interest for the AEC (Architecture, Engineering and Construction) branch and BIM (Building Information Modeling). Subject of this work is the geometric accuracy assessment of image-based change detection in indoor environments based on a BIM. Line features usually serve well as geodetic references in indoor scenes in order to solve for camera orientation. However, building edges are never perfectly built as planned and often geometrically generalized for BIM compliant representation. As a result, in this approach, line corresp

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Song, Jinhai, and Zhiyong Zhang. "Industrial Internet Intrusion Detection Method based on Cloud-Edge Collaboration." Frontiers in Science and Engineering 3, no. 3 (2023): 1–8. http://dx.doi.org/10.54691/fse.v3i3.4498.

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Industrial Internet security incidents occur frequently, and the amount of industrial data is increasing exponentially. Efficient and correct detection of attacks is critical to industrial Internet security. The method is based on the concept of cloud-edge collaboration to detect malicious behaviors. Firstly, the data is normalized and preprocessed to reduce the differences caused by different feature scales, then the deep neural network(DNN) is used to extract the features of massive data, and finally the softmax function is used for classification. In order to verify the effectiveness of the

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Xia, Liegang, Jun Chen, Jiancheng Luo, Junxia Zhang, Dezhi Yang, and Zhanfeng Shen. "Building Change Detection Based on an Edge-Guided Convolutional Neural Network Combined with a Transformer." Remote Sensing 14, no. 18 (2022): 4524. http://dx.doi.org/10.3390/rs14184524.

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Change detection extracts change areas in bitemporal remote sensing images, and plays an important role in urban construction and coordination. However, due to image offsets and brightness differences in bitemporal remote sensing images, traditional change detection algorithms often have reduced applicability and accuracy. The development of deep learning-based algorithms has improved their applicability and accuracy; however, existing models use either convolutions or transformers in the feature encoding stage. During feature extraction, local fine features and global features in images canno

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Li, Boqiang, Liang Qin, Feng Zhao, et al. "Research on Edge Detection Model of Insulators and Defects Based on Improved YOLOv4-tiny." Machines 11, no. 1 (2023): 122. http://dx.doi.org/10.3390/machines11010122.

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Edge computing can avoid the long-distance transmission of massive data and problems with large-scale centralized processing. Hence, defect identification for insulators with object detection models based on deep learning is gradually shifting from cloud servers to edge computing devices. Therefore, we propose a detection model for insulators and defects designed to deploy on edge computing devices. The proposed model is improved on the basis of YOLOv4-tiny, which is suitable for edge computing devices, and the detection accuracy of the model is improved on the premise of maintaining a high de

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PARSE, Milind, and Dhanya PRAMOD. "EDGE DETECTION TECHNIQUE BASED ON BILATERAL FILTERING AND ITERATIVE THRESHOLD SELECTION ALGORITHM AND TRANSFER LEARNING FOR TRAFFIC SIGN RECOGNITION." Scientific Journal of Silesian University of Technology. Series Transport 119 (June 1, 2023): 199–222. http://dx.doi.org/10.20858/sjsutst.2023.119.12.

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The traffic sign identification and recognition system (TSIRS) is an essential component for autonomous vehicles to succeed. The TSIRS helps to collect and provide helpful information for autonomous driving systems. The information may include limits on speed, directions for driving, signs to stop or lower the speed, and many more essential things for safe driving. Recently, incidents have been reported regarding autonomous vehicle crashes due to traffic sign identification and recognition system failures. The TSIRS fails to recognize the traffic signs in challenging conditions such as skewed

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Kim, Young Jae, Chan-Hyeok Park, and MyungKeun Yoon. "FILM: Filtering and Machine Learning for Malware Detection in Edge Computing." Sensors 22, no. 6 (2022): 2150. http://dx.doi.org/10.3390/s22062150.

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Machine learning with static-analysis features extracted from malware files has been adopted to detect malware variants, which is desirable for resource-constrained edge computing and Internet-of-Things devices with sensors; however, this learned model suffers from a misclassification problem because some malicious files have almost the same static-analysis features as benign ones. In this paper, we present a new detection method for edge computing that can utilize existing machine learning models to classify a suspicious file into either benign, malicious, or unpredictable categories while ex

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Stathopoulou, E. K., S. Rigon, R. Battisti, and F. Remondino. "ENHANCING GEOMETRIC EDGE DETAILS IN MVS RECONSTRUCTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 391–98. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-391-2021.

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Abstract. Mesh models generated by multi view stereo (MVS) algorithms often fail to represent in an adequate manner the sharp, natural edge details of the scene. The harsh depth discontinuities of edge regions are eventually a challenging task for dense reconstruction, while vertex displacement during mesh refinement frequently leads to smoothed edges that do not coincide with the fine details of the scene. Meanwhile, 3D edges have been used for scene representation, particularly man-made built environments, which are dominated by regular planar and linear structures. Indeed, 3D edge detection

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