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检索条件"主题词=Detection Algorithms"

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R-Wave detection Algorithm Based on Central Difference and Adaptive Multi-Threshold

R-Wave Detection Algorithm Based on Central Difference and A...

引用

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

作者： Ke Xu Huibin Wang Sikai Guo Wenping Chen College of Information Science and Engineering Hohai University HHU Nanjing China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

As a non-invasive method for diagnosing heart diseases, electrocardiography (ECG) relies on R-wave detection as a foundational step for other ECG waveform analysis. To further meet the demands for accuracy and real-time detection of R-waves in automatic ECG analysis, this paper proposes a low-complexity, fast, and reliable real-time R-wave detection algorithm. An R-wave detection method based on central difference and adaptive multi-threshold is designed. First, this paper proposes the use of central difference for R-wave enhancement, effectively improving the detection capability for low amplitude and low slope R-waves. Secondly, an adaptive multi-threshold strategy is proposed, which adaptively adjusts the current detection threshold based on the detected R-wave amplitude and RR interval, making it suitable for R-wave detection in cases of sudden changes in RR interval and R-wave amplitude. The proposed algorithm’s performance was evaluated using ECG signals from the MIT-BIH database, achieving an overall sensitivity, positive predictive value, and detection error rate of 99.73%, 99.82%, and 0.46%, respectively. The processing time for a 30-minute ECG recording is approximately 0.52 seconds. The results indicate that the algorithm is accurate, efficient, and suitable for real-time automatic ECG analysis.

关键词： Accuracy Sensitivity Machine learning algorithms Electrocardiography Prediction algorithms Approximation algorithms Real-time systems Recording Reliability detection algorithms

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detection of False Data Injection Attacks in New Energy Systems Based on Supervised Learning

Detection of False Data Injection Attacks in New Energy Syst...

引用

Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Tong Li Lei Wang Jian Chen Yang Liu Hongwei Zhao Hongbi Geng State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China State Grid Liaoning Electric Power Research Institute Shenyang China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

In recent years, with the rapid development of China’s new energy industry, and to the monitoring and data acquisition (SCADA) system brings a huge network security risks. False Data Injection Attack (FDIA) is a serious threat to the secure and stable operation of smart grids. In this paper, an FDIA detection algorithm that integrates unsupervised and supervised learning is proposed to address the problems of scarce labelled data and extreme imbalance between normal and attack samples in FDIA detection. Compared with other FDIA detection algorithms, the proposed method enhances the stability of the FDIA detection model in the case of scarce labelled samples and unbalanced data, improves the recognition accuracy of FDIA and reduces the false alarm rate.

关键词： Accuracy Supervised learning SCADA systems Power system stability Network security Stability analysis Smart grids detection algorithms Thermal stability Monitoring

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Research on the detection Algorithm for Abnormal Crowd Behaviors Based on an Enhanced SlowFast Model

Research on the Detection Algorithm for Abnormal Crowd Behav...

引用

International Conference on Computers, Communications, and Systems (ICCCS)

作者： Yueping Peng Hexiang Hao Tongtong Zhou Baixuan Han Wenji Yin Information Engineering Collage Engineering University of PAP Xian China Engineering University of PAP Xian China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Aiming at the issues of inaccurate detection of behavioral classification in crowd abnormal behaviors detection algorithms and large model computation capacity, a crowd abnormal behaviors detection algorithm based on the improved SlowFast model is proposed. By changing the convolutional structure of the network to a Depthwise-separable convolution, adding a hybrid domain attention mechanism, and inserting Dropout to inhibit the overfitting phenomenon, we can enhance the network's precision of detection and classification of behaviors while ensuring the computational capacity. Experiments show that the method in this paper can detect and identify five types of behaviors in public places: normal, scattered, speeding up in the same direction, sudden gathering, and group fighting. Compared with the original SlowFast model and other algorithms, the precision is improved, and the number of detected frames per second can be up to 40.5 fps, which meets the real-time requirements, providing a new idea for the determination and classification of abnormal behaviors of crowds.

关键词： Attention mechanisms Convolution Communication systems Computational modeling Real-time systems Classification algorithms detection algorithms

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Pedestrian detection Algorithm Based on Multi-Scale Fusion and Dual-Backbone

Pedestrian Detection Algorithm Based on Multi-Scale Fusion a...

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Deshuai Bao Wenzhong Zhu School of Computer Science & Engineering Sichuan University of Science & Engineering Yibin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Addressing the limitations of visible light singlemodal target detection algorithms, which are affected by external factors like illumination, and infrared single-modal algorithms, which lack complete target information, we propose a pedestrian detection algorithm based on dual-backbone and multi-modal multi-scale fusion, building upon *** network employs a dual-backbone structure derived from the YOLOv8n lightweight network. An SMMF(Scale-Multiple Multimodal Feature Fusion) module is designed to fuse features from both modalities at multiple scales. The GhostConv module is introduced to enrich features and reduce computational cost. Additionally, the SBAp2 is integrated into the network neck, enhancing the fusion of deep and shallow features and leveraging both deep semantic information and shallow image location information. The ShapeIoU is adopted as the loss function, replacing the original bounding box regression loss, to incorporate the shape and scale of the bounding box into the loss calculation, thereby improving target detection accuracy. Experimental results indicate that the improved algorithm achieves an AP0.5 of 78.5% and an FPS of 156.3. Compared to the original visible light single-modal and infrared single-modal YOLOv8n algorithms, the missed detection rates were reduced by 30.1% and 13.4%, respectively, while the AP0.5 is increased by 21.2% and 8.2%, demonstrating its superiority over the single-modal YOLOv8n detection algorithm.

关键词： Pedestrians Accuracy Shape Semantics Object detection Reconnaissance Real-time systems Neck detection algorithms Standards

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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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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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A Real-Time Video detection Algorithm for UAV Based on YOLOv8

A Real-Time Video Detection Algorithm for UAV Based on YOLOv...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Xin Nie Qili Wu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Aiming at the common problem of small target detection in UAV real-time video surveillance, this paper proposes a UAV video detection algorithm based on improved YOLOv8 framework. By embedding a feature enhancement module specifically for small targets in the original YOLOv8 architecture, the algorithm effectively improves the recognition degree of the network for small targets. In order to avoid the excessive expansion of network parameters, the header network is simplified, the convolutional layer is removed and the number of channels is reduced to maintain the computational efficiency of the algorithm. Then this paper introduces the Channel Space Attention Mechanism module (CSAM), which makes the network pay more attention to the small target-dense region on the feature map by optimizing the weight distribution, thus enhancing the pertinence of target detection. In addition, a location-based label assignment strategy (LB-SimOTA) is proposed, which assigns different weights according to the intersection ratio (IoU) between the prediction frame and the real frame to optimize the overall quality of the network prediction frame. Experimental results on the VisDrone2019 dataset containing many small targets show that compared with the benchmark model YOLOv8-S, the proposed algorithm improves the accuracy of vehicle and pedestrian detection tasks by 8.44% and 8.12%, respectively, and achieves a frame rate of up to 90 FPS while maintaining the detection speed.

关键词： Accuracy Pedestrians Target recognition Object detection Streaming media Autonomous aerial vehicles Feature extraction Video surveillance Real-time systems detection algorithms

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An Improved SSD Deep Network-Based Algorithm for Eye State detection

An Improved SSD Deep Network-Based Algorithm for Eye State D...

引用

International Conference on Electronic Engineering and Informatics (EEI)

作者： Weikun Dai Jianbin Xiong Qi Wang Xiangjun Dong School of Automation Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Aiming at the problem of poor real-time performance in current eye state detection algorithms, a deep learning algorithm-improved single-shot multibox detector (SSD) with both real-time performance and detection accuracy is proposed. Firstly, the lightweight network MobileNetV2 replaces the original feature extraction network VGG16 to improve the detection speed. Then, spatial and channel squeeze-excitation network (scSENet) attention module is introduced to enhance the spatial features and channel features of the eye to improve the detection accuracy. The proposed method is tested on the public data set Closed Eyes in the Wild (CEW), achieving a mAP value of 96.12%, which is higher than current mainstream methods. The experimental results show that the algorithm has high accuracy and efficient detection speed in the task of eye state detection.

关键词： Deep learning Accuracy Detectors Feature extraction Real-time systems detection algorithms Informatics

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STOE-YOLOv5s: A Hard Hat Wearing detection Algorithm Based on Improved YOLOv5s

STOE-YOLOv5s: A Hard Hat Wearing Detection Algorithm Based o...

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Robotics and Computer Vision (ICRCV), International Conference on

作者： Tao Hu Wei Huang Jianghao Wei Qiang Liu Jiahuan Huang Jie Yu School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798331527426

ISBN: (纸本)9798331527433

To address the existing issues of missed detection and false detection of hard hats by mainstream detection models in complex environments, an improved hard hat detection algorithm, STOE-YOLOv5s based on YOLOv5s, has been proposed. Firstly, enhance the detection header structure, add multi-scale target detection layers, and improve the detection capability for small targets; Secondly, a BottleNeck Transformer (BoT3) module based on self-attention is added to the backbone network to enhance the model's ability to extract global features; Then, to solve the problem of positive and negative sample matching, a Simplified Optimal Transport Assignment (SimOT A) strategy is used to obtain the optimal matching result in the global range; Finally, an Efficient Multi-Scale Attention (EMA) mechanism is fused into the C3 module of the network neck to enhance the model's ability to capture features-rich areas. Experimental results on the hard hat dataset show that the mAP of the improved algorithm reaches 96.0%, and the recall rate reaches 91.8%. Compared with the original YOLOv5s algorithm, it has increased by 1.7 and 2.9 percentage points respectively, proving the effectiveness and advancement of the improved algorithm.

关键词： YOLO Head Accuracy Computational modeling Feature extraction Transformers Safety Resource management detection algorithms Optimal matching

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