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检索条件"主题词=detection algorithms"

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Develop a liquid level detection algorithm for infusion bottles based on Ghost-YOLOv4

Develop a liquid level detection algorithm for infusion bott...

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Computer Information Science and Application Technology (CISAT), International Conference on

作者： Liang Jianfeng Thelma D. Palaoag Department of Computer Science University of the Cordilleras Baguio City Philippines College of Electronic and Information Engineering University of Beibu Gulf Qinzhou City China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In view of the problems of weak anti-interference ability, low detection accuracy and high algorithm complexity that make it difficult to ensure real-time performance in current infusion bottle level detection methods, this paper proposes an infusion bottle level detection method based on the Ghost-YOLOv4 model. First, the collected infusion bottle level image sample data is enhanced to improve the versatility of the sample data, and the Labelimg tool is used to annotate the images and create data sets. Then, the lightweight Ghost main network structure is used to replace the CSPDarkenet53 main network structure of the original YOLOv4 model, thereby reducing the number of model parameters and improving the detection speed. Secondly, the regression box loss function of the Ghost-YOLOv4 model is optimized to reduce the impact of excessive linear calculations after replacing the main network, resulting in the loss of some feature information, on the detection accuracy, thereby improving the detection accuracy. Finally, the experimental results show that the improved Ghost-YOLOv4 model has about 85% fewer parameters than the original YOLOv4 model, and the detection speed is increased by about 115%. The Ghost-YOLOv4 model with optimized loss function has higher recognition frame positioning accuracy, which can provide better technical support for the automatic, high-quality and rapid detection of liquid level in infusion bottles.

关键词： Information science Accuracy Liquids Computational modeling Object detection Feature extraction Real-time systems Complexity theory detection algorithms

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Single-Frame Infrared Point Target detection Based on YOLO-v5

Single-Frame Infrared Point Target Detection Based on YOLO-v...

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Bendong Zhao Fengshun Lu Xingzhi Hu Mingjie Li Jianqi Lai China Aerodynamics Research and Development Center Mianyang Sichuan China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

In order to solve the problem of target detection in infrared images, a single-frame infrared point target detection method based on YOLO-v5 is proposed. Firstly, the problems and difficulties of single-frame infrared target detection are analyzed, and the principles of three commonly used single-frame detection algorithms based on background prediction are introduced. Then the algorithm principle and network structure of YOLO-v5 are elaborated in detail. Finally, 12 groups of infrared images with different backgrounds are constructed for target detection experiments, and the experimental results prove that YOLO-v5 has great advantages over traditional single-frame detection algorithms.

关键词： Analytical models Automation Object detection Network analyzers Prediction algorithms detection algorithms Complex systems Information technology Periodic structures

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E-DDoS: An Evaluation System for DDoS Attack detection

E-DDoS: An Evaluation System for DDoS Attack Detection

引用

International Conference on Network Protocols

作者： Kaiwen Chi Xiaohui Xie Yannan Hu Dongyang Zhao Yuming Xie Liang Zhang Yong Cui Department of Computer Science and Technology Tsinghua University Beijing China Zhongguancun Laboratory Beijing China AI4NET Lab Huawei Technologies Company Ltd Beijing China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

Research in the area of Distributed Denial of Service (DDoS) attack detection is of paramount importance in the field of network security. Many existing studies employ static evaluation methods that fail to account for the reduction in accuracy due to the impact of inference latency on the timeliness of classification results. Furthermore, these studies frequently rely on simulated datasets for experimentation, which often lack the complexity and challenge of real-world attacks. These limitations significantly hinder the applicability of such research in practical scenarios. To overcome these challenges, we propose an evaluation methodology for real-time DDoS attack detection incorporating inference latency considerations. Additionally, we have developed a challenging DDoS dataset named THU-DDoS2024 and conducted experiments across four classification algorithms. This novel evaluation method and the newly generated dataset are integrated into an evaluation framework named E-DDoS. Leveraging E-DDoS, the “Intelligent Classification of High-Speed Network Traffic (ICNT)”, Grand Challenge was initiated. This event aims to motivate both academic and industrial sectors to delve into high-speed traffic classification tasks, thereby enhancing the applicability of research outputs to real-world applications.

关键词： Accuracy Protocols High-speed networks Organizations Network security Denial-of-service attack Real-time systems Classification algorithms Computer crime detection algorithms

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Reducing Unfairness in Distributed Community detection

Reducing Unfairness in Distributed Community Detection

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IEEE International Conference on Data Mining (ICDM)

作者： Hao Zhang Malith Jayaweera Bin Ren Yanzhi Wang Sucheta Soundarajan Syracuse University Northeastern University William & Mary

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Big graph data mining and processing have emerged as a crucial area of study. Distributed graph frameworks are commonly employed to process such big graph data in various applications. These frameworks have proven to be highly effective in improving both the accuracy and efficiency of processing large-scale graph data, but little attention has been paid to the algorithmic fairness of such methods. In this paper, we propose a novel graph reweighting algorithm, Homophily-Based Graph Reweighting (HBGR), which can be used with different distributed community detection frameworks. The findings of our study demonstrate that HBGR can significantly enhance the fairness of detected community results, without altering the overall distributed community detection algorithm workflow. Our analysis demonstrates that HBGR outperforms traditional performance-based distributed graph data processing frameworks in terms of fairness across 13 real social network datasets. This enhancement enables us to achieve fairness levels that are comparable, or even superior, to those achieved by linear community detection algorithms while maintaining good efficiency performance. Additionally, we examine the causes of unfairness in distributed community detection algorithms and conduct an interpretability analysis of HBGR's improved fairness performance. Finally, we provide a comprehensive evaluation of the trade-offs between efficiency, accuracy, and fairness in distributed community detection algorithms.

关键词： Accuracy Social networking (online) Distributed databases Data processing Data mining detection algorithms

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Topology Error detection for Defensive Islanding of Power Grids

Topology Error Detection for Defensive Islanding of Power Gr...

引用

IEEE General Meeting Power& Energy Society

作者： Abdelrahman Abdelkader Mohammed Benidris Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Resilience improvement through defensive islanding requires multiple simultaneous topological changes after an event to exclude damaged components and transform the grid into power balanced islands. Topology error identification is challenging under such conditions given the limited availability of measurements and uncertainty of events. This paper proposes a new approach for machine learning-aided topology error detection and identification for defensive islanding. The approach combines conventional data error detection with a data driven model to distinguish error types and identify the erroneous component. The topology check is done on two different levels. In the lower level (decoupled islands), a state estimation problem is solved for error detection in each of the smaller grids. Solving decoupled small systems requires less time and computation power, which is essential for the time sensitive post-disaster task at hand. The identification step, however, is at the high level (connected grid) due to the possibility of the topology error being in the links between the islands. Approach implementation is possible through numerous State Estimation (SE) and topology validation approaches. In this work, a DC SE is performed since the concern here is the connectivity rather than accurate power flow calculations. A neural network model is used to validate topology considering data and system uncertainties. A case study utilizing the proposed method is performed on different topological variations of the IEEE 118-Bus system. Scarcity and uncertainty of data are also considered. The results show promising potential of the proposed algorithm.

关键词： Accuracy Uncertainty Islanding Network topology Measurement uncertainty Termination of employment Topology State estimation detection algorithms Resilience

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Integrated Vision-Aided Precision Navigation and Obstacle detection Sensing Pipeline for UAM Approach and Landing

Integrated Vision-Aided Precision Navigation and Obstacle De...

引用

Conference on Digital Avionics Systems (DASC)

作者： Enrico Miccio Paolo Veneruso Roberto Opromolla Giancarmine Fasano Carlo Tiana Giacomo Gentile Dept. of Industrial Engineering University of Naples Federico II Naples Italy Vision Systems Collins Aerospace Portland OR USA Applied Research and Technology Collins Aerospace Rome Italy

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

Enabling obstacles detection capabilities onboard manned or unmanned aircraft during the landing phase using vision sensors is a challenging task. In fact, detecting and tracking static and moving objects such as other vehicles which typically lie below the horizon is hindered by the cluttered background and the ownship motion. Motion-based detection approaches (such as the ones exploiting homography) may attain a reasonable performance in flat scenarios, but encounter challenges in three-dimensional environments due to the highly variable distance of imaged features. This paper explores obstacle detection algorithms for landing considering both motion-based algorithms using homography, and appearance-based techniques built on Convolutional Neural Networks (CNNs), with the idea of combining them. The obstacle detection function is conceived to complement a previously developed precision navigation system for landing, exploiting the same vision sensors. Different approaches are considered for static and moving obstacles. The pipeline has been validated through both synthetic and flight-testing data showing promising results for future more structured integrations.

关键词： Navigation Tracking Pipelines Vision sensors Aerospace electronics Feature extraction Convolutional neural networks detection algorithms Aircraft

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Local Community detection from Noisy Graph Streams

Local Community Detection from Noisy Graph Streams

引用

Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Han Yang Lingxiang Wang Qingren Wang Huabin Wang Yiwen Zhang Hong Zhong Dengcheng Yan School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

The demand for real-time graph analysis has encouraged the research of online community detection methods. However, these methods often assume clean data, which is uncommon in reality. How to further improve the performance and robustness of online community detection for real-world networks has garnered the great attention. In this paper, we propose NgsLCD, an algorithm that identifies valid edges in noisy streams to expand communities and mitigate noise effects. It also uses a node-community similarity metric to filter out incorrectly added nodes. Tests on real networks show NgsLCD reduces noise impact and maintains robust detection performance across varying noise rate.

关键词： Image edge detection Noise Filtering algorithms Robustness Real-time systems Noise measurement detection algorithms Streams Software engineering Noise level

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A survey on the modern research on deep learning-based object detection

A survey on the modern research on deep learning-based objec...

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Computer Vision and Machine Intelligence (CVMI), International Conference on

作者： T Selvamuthukumar P Dhanalakshmi K Vijayalakshmi R Abinaya Annamalai University Chidambaram Tamilnadu India SR Gudlavalleru Engineering College Vijayawada Andhra Pradesh India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Detecting objects remains a vital and challenging aspect within computer vision applications. Substantial progress in detecting objects is made by enhancing how objects are represented and by utilizing deep models of neural networks. This paper delivers a broad survey on the advancements on object detection within the deep learning era in recent years. Object detection algorithms are generally branded into two broad types centered on their training approach: single-stage detection algorithms and two-stage detection algorithms, each utilizing unique methods for pinpointing objects in images. We examine the underlying rationale of these algorithms by conducting experiments to compare various aspects, including quality metrics, trade-offs between speed/accuracy, and modelling practices. This study also provides a comparative assessment of leading convolutional neural networks (CNNs) used in detecting objects, emphasizing each model’s strengths/limits and presenting key conclusions. To foster a deeper understanding, we also identify areas for future research and potential advancements in object detection models within the domain of deep learning.

关键词： Deep learning Surveys Training Measurement Computer vision Reviews Neural networks Object detection Object recognition detection algorithms

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Research on Kiwifruit detection Algorithm Based on Faster-YOLOv8n

Research on Kiwifruit Detection Algorithm Based on Faster-YO...

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Artificial Intelligence and Computer Engineering (ICAICE), International Conference on

作者： Dan Zhao Jiaxing Wang Wei Shan College of Physics and Electronic Information Huaibei Normal University Anhui Provincial Key Laboratory of Intelligent Computing and Application Huaibei China

ISBN: (数字)9798331528911

ISBN: (纸本)9798331528928

In view of the complex structure of YOLOv8n model and the large amount of parameters and calculations of the existing kiwifruit detection algorithm, it is difficult to deploy in the terminal. This paper proposes a lightweight kiwifruit detection algorithm model Faster-YOLOv8n based on YOLOv8n. That is, all C2f modules in the YOLOv8n model are merged with the FasterNet lightweight network structure to form a new C2f-Faster module to reduce the consumption of computing resources. The experimental results show that the average detection accuracy mAP of the Faster-YOLOv8n algorithm on the self-made kiwifruit dataset collected in this paper is 96.9 %. Compared with the YOLOv8n algorithm, the model size is reduced by 32 %,the calculation amount is reduced by 22.2 %, the parameter quantity is reduced by 29.6 %, and the detection speed is increased by 26.7 %.Therefore, the detection method proposed in this paper reduces the consumption of computing resources and provides a theoretical reference for the lightweight detection of kiwifruit.

关键词： YOLO Accuracy Image recognition Fuses Computational modeling Production detection algorithms Artificial intelligence

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Community detection and Centrality Measures in Alzheimer’s Disease Network Analysis

Community Detection and Centrality Measures in Alzheimer’s ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Yaswanth Chowdary Thotakura Kalyan Kumar Doppalapudi Srinivas Reddy Gopavarapu Geetha Siva Srinivas Gollapalli Jaya Lakshmi Tangirala Department of CSE SRM University-AP India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This project intricately explores network analysis in the context of Alzheimer’s disease, emphasizing community detection and centrality as a pivotal facet in data research on complex networks. Community structure, an essential component of complex networks, has progressively emerged as a focus of study interest. The community detection algorithm is useful for examining the network’s true structure. The paper highlights the importance of community identification strategies in understanding network behavior and topology analysis by classifying them into non-overlapping and overlapping methods. Many ways are looked at, such as those built on different factors separation into parts, group linking, tag movement and gradual steps partitioning. The process of finding out what sicknesses, like Alzheimer’s disease someone has also highlights the advantages of knowing and working with many different abilities, skills working as a group to fix issues, and sharing what we know in a community. It present a way to look at community splits using a measure called modularity. This focuses on how it can be used to check layout, steer refinement, and comprehend network dynamics. In this paper, we look into measures of importance, especially coreness centrality. We also talk about core-periphery structures, k-shell decomposition, and extensions like the all-around score and hybrid centrality measures, which provide useful tools for determining influential nodes in complex networks.

关键词： Heart Social networking (online) Shape Layout Network analyzers Complex networks Social factors Topology Alzheimer's disease detection algorithms

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