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检索条件"主题词=Detection Algorithms"

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Research on an Intelligent Obstacle detection Algorithm for High Voltage Lines Based on Improved YoLo Algorithm

Research on an Intelligent Obstacle Detection Algorithm for ...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Yanghong Qin School of Artificial Intelligence Chongqing Three Gorges Vocational College Chongqing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In this paper, an intelligent obstacle detection method based on improved YOLO (You Only Look Once) algorithm is proposed to improve the efficiency and safety of automated inspection of high-voltage transmission line corridors in power system. The operating environment of high-voltage lines is complex and changeable, and the existence of obstacles such as kites, nesting birds and hanging foreign objects poses a major threat to the stability and safety of the power system. Traditional manual inspection is not only inefficient, but also faces many security risks. Therefore, it is particularly important to develop an intelligent detection algorithm that can identify and locate obstacles in high voltage lines in real time and accurately. This study first analyzes the limitations of the original YOLO algorithm in the high-voltage line obstacle detection scenario, including the lack of precision for small target detection and the poor robustness against complex background. In order to solve these problems, we propose the following improvement measures: First, the Feature Pyramid Network (FPN) structure is introduced to enhance the detection ability of multi-scale obstacles; The second is to use the deep residual learning framework to solve the problem of gradient disappearance in the deep network training process, and further improve the detection accuracy of the model. The experimental results show that the improved YOLO algorithm has achieved significant improvement in the obstacle detection task of high-voltage lines, and has obvious advantages in Average Precision (AP) index compared with other classical target detection algorithms.

关键词： YOLO Accuracy High-voltage techniques Power system stability Inspection Feature extraction Robustness Real-time systems Safety detection algorithms

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An Adaptive Step detection Algorithm for Smartwatch with Deep Learning-based Human Activity Recognition

An Adaptive Step Detection Algorithm for Smartwatch with Dee...

引用

International Conference on Indoor Positioning and Indoor Navigation (IPIN)

作者： Sohee Park Jae Hong Lee Chan Gook Park Dept. of Aerospace Engineering Seoul National University Seoul Republic of Korea Dept. of Aerospace Engineering/ASRI Seoul National University Seoul Republic of Korea

ISBN: (数字)9798350366402

ISBN: (纸本)9798350366419

As the use of smartwatches continues to grow, improving the accuracy of tracking for smartwatch users has become increasingly important. Smartwatches are worn on the wrist, which means the sensors may detect free and independent hand movements while walking. This creates challenges for pedestrian dead reckoning (PDR) using the inertial sensors embedded in smartwatches because the data may lose information on walking characteristics. Therefore, it is essential to identify the user’s walking state first, whether moving or stationary, and then conduct precise step detection, which is a critical factor in reducing errors in PDR. In this study, we propose an adaptive step detection algorithm tailored to four categorized user activity states: walking, running, hand movements while standing, and hand movements while walking. We conducted human activity recognition (HAR) with these four categories to identify the user’s state and decide the appropriate method for step detection. To enable implementation on mobile devices, we propose a lightweight deep learning-based HAR network utilizing a grouped convolutional layer. The proposed network reduces the number of trainable parameters by approximately one-third compared to conventional CNN structures while achieving $\mathbf{9 6. 4 5 \%}$ accuracy in activity classification. For adaptive step detection, we utilize gyroscope data for normal walking, accelerometer data for running, and walking frequency obtained via fast Fourier transform (FFT) from normal walking data for segments of walking interspersed with hand movements. By applying these tailored step detection methods to each walking scenario, we enhance step detection accuracy, thereby improving smartwatch user position and distance estimation in overall PDR.

关键词： Legged locomotion Accelerometers Wrist Accuracy Fast Fourier transforms Convolution Angular velocity Gyroscopes Human activity recognition detection algorithms

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detection of False Data Injection Attacks in Cyber-Physical Systems

Detection of False Data Injection Attacks in Cyber-Physical ...

引用

IEEE International Symposium on Information Theory

作者： Souvik Das Avishek Ghosh Debasish Chatterjee Systems and Control Engineering Indian Institute of Technology Bombay India

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

This article addresses the problem of detecting, with high probability, the presence of a large class of history-dependent false data injection actuator attacks in cyber-physical systems (CPSs) modeled as stochastic linear time-invariant systems. Among the primary contributions is the introduction of a new detection algorithm or scheme that leverages the fundamental idea of separating or classifying two classes of state trajectories: The first class comprises state trajectories generated by the CPS under attack situations, while the second class includes state trajectories generated under nominal conditions. The existence and non-existence of such a separator are studied and these results are asymptotic. We then establish finite-time guarantees associated with the detection algorithm or scheme. A numerical example is provided to demonstrate the theory.

关键词： Actuators Particle separators Stochastic processes Cyber-physical systems Data models Trajectory detection algorithms

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An Improved Object detection Algorithm Based on YOLOv10

An Improved Object Detection Algorithm Based on YOLOv10

引用

International Conference on Computer and Communications (ICCC)

作者： Wenhai Liu Song Xiao Lin Fu Shenyang Institute of Computing Technology University of Chinese Academy of Sciences Shenyang China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

Object detection is still an important research topic in computer vision. For years, YOLO series object detection framework has been the object of research and improvement by many researchers since its appearance. In this paper, the improved space-to-deepth downsampling convolution algorithm is used to replace the convolution downsampling algorithm of YOLOv10 aiming at the shortcoming that the downsampling convolution process in the original detection algorithm can not retain more spatial information. We introduced RepCSP module to improve C2f module in YOLOv10. By mixing up linear attention mechanism, the feature extraction module and linear attention mechanism are integrated, and the feature extraction ability of the model is improved. Combined with the improved convolutional downsampling network, the improved C 2fRepCSP module and the integrated linear attention mechanism, the model is trained and evaluated on VOC 2007 and VOC 2012 data sets, and the results show that the detection accuracy of the model has been improved.

关键词： YOLO Computer vision Attention mechanisms Accuracy Convolution Detectors Feature extraction Data models detection algorithms

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Quickest detection in High-Dimensional Linear Regression Models via Implicit Regularization

Quickest Detection in High-Dimensional Linear Regression Mod...

引用

IEEE International Symposium on Information Theory

作者： Qunzhi Xu Yi Yu Yajun Mei School of Industrial and Systems Engineering Georgia Institute of Technology Department of Statistics University of Warwick Department of Biostatistics School of Global Public Health New York University

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

In this paper, we consider the quickest detection problem in high-dimensional streaming data, where the unknown regression coefficients might change at some unknown time. We propose a quickest detection algorithm based on the implicit regularization algorithm via gradient descent, and provide theoretical guarantees on the average run length to false alarm and detection delay. Numerical studies are conducted to validate the theoretical results.

关键词： Linear regression Numerical models Delays detection algorithms Information theory

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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...

引用

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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Reducing Unfairness in Distributed Community detection

Reducing Unfairness in Distributed Community Detection

引用

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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Image edge detection based on improved ant colony algorithm

Image edge detection based on improved ant colony algorithm

引用

Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Yuehao Yan Juan Hui Gaohui Kong Shuaishuai Wang Liyi Feng Yujie Zhao State Grid Henan Electric Power Company Xingyang Power Supply Company Xingyang China Zhengzhou Xinda Advanced Technology Research Institute Zhengzhou China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Image edge detection is a necessary work in object recognition, feature extraction and structure analysis. Firstly, an improved ant colony algorithm is proposed to solve the problems of partial path selection, low accuracy and low efficiency of traditional ant colony algorithm. The initial pheromone, heuristic function, pheromone update rule and state transition parameters of ant colony algorithm were improved. Finally, the experimental simulation verifies that two classical images are selected to carry out adaptive edge extraction, and the edge detection effect is compared with the traditional detection algorithm. Experimental results show that the improved algorithm can achieve complete and continuous image edge extraction, and verify the effectiveness of the algorithm.

关键词： Accuracy Image edge detection Heuristic algorithms Noise Signal processing algorithms Clustering algorithms Feature extraction Object recognition detection algorithms Intelligent control

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Robust Multimodal Cough and Speech detection using Wearables: A Preliminary Analysis

Robust Multimodal Cough and Speech Detection using Wearables...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yuhan Chen Jeffrey Barahona Iype Eldho Yanbing Yu Riyaz Muhammad Bill Kutsche Michelle L. Hernandez Delesha Carpenter Alper Bozkurt Edgar Lobaton Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA Murata Electronics North America Inc. Smyrna GA USA Department of Pediatrics School of Medicine University of North Carolina at Chapel Hill Chapel Hill NC USA Division of Pharmaceutical Outcomes and Policy Eshelman School of Pharmacy University of North Carolina at Chapel Hill Chapel Hill NC USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Cough detection is a crucial tool for long-term monitoring of respiratory illnesses. While clinical methods are accurate, they are not available in a home-based setting. In contrast, wearable devices offer a more accessible alternative, but face challenges in ensuring user speech privacy and detecting coughs accurately in real-world settings due to potential poor audio quality and background noise. This study addresses these challenges by developing a small-size multimodal cough and speech detection system, enhanced with an Out-of-Distribution (OOD) detection algorithm. Through our analyses, we demonstrate that combining transfer learning, a multimodal approach, and OOD detection techniques significantly improves system performance. Without OOD inputs, the system shows high accuracies of 92.59% in the in-subject setting and 90.79% in the cross-subject setting. With OOD inputs, it still maintains overall accuracies of 91.97% and 90.31% in these respective settings by incorporating OOD detection, despite the number of OOD inputs being twice that of In-Distribution (ID) inputs. This research are promising towards a more efficient, user-friendly cough and speech detection method suitable for wearable devices.

关键词： Voice activity detection Privacy Accuracy System performance Transfer learning Wearable devices Engineering in medicine and biology detection algorithms Monitoring Faces

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SM-YOLOv8 detection algorithm for traffic signs

SM-YOLOv8 detection algorithm for traffic signs

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Computer Technology and Information Science (ISCTIS), International Symposium on

作者： Junlong Zhang Wenhua Han College of Automation Engineering Shanghai University of Electric Power Shanghai China

ISBN: (数字)9798350354560

ISBN: (纸本)9798350354577

To address the issues of missed detection and false detection in traffic sign detection during daily driving, an improved SM-YOLOv8 algorithm is proposed. In the backbone, Max-Pooling down sampling is used to preserve important feature information and reduce feature loss. The MSCA convolutional attention is embedded to aggregate local and global information for selecting and extracting target features at various scales. The multi-scale cross-layer feature fusion structure SM-Neck is designed to enhance the fusion of semantic information at different levels and effectively capture target features at various scales. The detection performance is further improved by using the Inner method to enhance CIoU. On the TT100K dataset, the accuracy, recall rate, and mAP value are improved by 11.6%, 9.3%, 9.8%, and 7.4% respectively, with a 30% reduction in parameters. Experimental results demonstrate that the proposed method exhibits advantages of high accuracy and low parameter count compared to the original model and other algorithms.

关键词： Cross layer design Information science Accuracy Attention mechanisms Computational modeling Aggregates Semantics Feature extraction Data mining detection algorithms

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