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检索条件"主题词=detection algorithms"

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A Wavelet Feature Based SAR Ship detection Algorithm in Sparse Framework

A Wavelet Feature Based SAR Ship Detection Algorithm in Spar...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Zongsi Chen Huiping Lin Feng Xu Key Laboratory for Information Science of Electromagnetic Waves (MoE) FudanUniversity Shanghai China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

This study proposes a novel method for ship target detection in Synthetic Aperture Radar (SAR) images. Traditional ship detection methods rely heavily on manually designed features, which are less robust. In contrast, deep learning-based detection methods, which can autonomously learn parameters and extract features, have achieved promising results in SAR ship detection. However, SAR ship target detection faces challenges such as complex backgrounds, noise interference, and scale variation. These issues are particularly pronounced in near-shore scenes where background interference and dense ship arrangements lead to occlusion. Furthermore, unlike optical RGB images that have three independent channels, SAR images have a single-channel problem, making it difficult for Convolutional Neural Networks (CNNs) to extract features from limited channel information. Additionally, most mainstream object detection methods use dense anchor boxes either directly or indirectly, which introduces a large amount of background regions with no ships in sparse SAR images. To address these issues, we first apply specific data augmentation techniques to handle background interference and occlusion in SAR ship targets. Next, we use wavelet transform techniques to enrich the channel information of SAR images. Finally, we adopt a sparse framework to cater to the sparse nature of SAR ship targets. Experiments conducted on the SSDD and HRSID SAR ship datasets demonstrate that our method has significant advantages compared to other advanced detection algorithms.

关键词： Wavelet transforms Object detection Interference Feature extraction Data augmentation Radar polarimetry Data mining Marine vehicles detection algorithms Synthetic aperture radar

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How to Count Coughs: An Event-Based Framework for Evaluating Automatic Cough detection Algorithm Performance

How to Count Coughs: An Event-Based Framework for Evaluating...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Lara Orlandic Jonathan Dan Jérôme Thevenot Tomas Teijeiro Alain Sauty David Atienza Embedded Systems Laboratory (ESL) of EPFL Lausanne Switzerland BCAM - Basque Center for Applied Mathematics Bilbao Spain Lausanne University Hospital (CHUV) Lausanne Switzerland

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Chronic cough disorders are widespread and challenging to assess because they rely on subjective patient questionnaires about cough frequency. Wearable devices running Machine Learning (ML) algorithms are promising for quantifying daily coughs, providing clinicians with objective metrics to track symptoms and evaluate treatments. However, there is a mismatch between state-of-the-art metrics for cough counting algorithms and the information relevant to clinicians. Most works focus on distinguishing cough from non-cough samples, which does not directly provide clinically relevant outcomes such as the number of cough events or their temporal patterns. In addition, typical metrics such as specificity and accuracy can be biased by class imbalance. We propose using event-based evaluation metrics aligned with clinical guidelines on significant cough counting endpoints. We use an ML classifier to illustrate the shortcomings of traditional sample-based accuracy measurements, highlighting their variance due to dataset class imbalance and sample window length. We also present an open-source event-based evaluation framework to test algorithm performance in identifying cough events and rejecting false positives. We provide examples and best practice guidelines in event-based cough counting as a necessary first step to assess algorithm performance with clinical relevance.

关键词： Body sensor networks Machine learning algorithms Accuracy Event detection Machine learning Length measurement Classification algorithms Wearable devices detection algorithms Guidelines

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Efficient Anomaly detection Algorithm for Operational Data Based on Fuzzy Cognitive Map

Efficient Anomaly Detection Algorithm for Operational Data B...

引用

Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC), International Conference on

作者： Xin Zhao Changda Huang Marketing Service Center of State Grid Xinjiang Electric Power Co. LTD Urumqi China

ISBN: (数字)9798331530280

ISBN: (纸本)9798331530297

In order to improve the accuracy and efficiency of anomaly detection in operational data, an efficient anomaly detection algorithm for operational data based on fuzzy cognitive maps is proposed. Clean, denoise, and standardize raw operational data; Extracting concept nodes based on fuzzy cognitive maps; Constructing a fuzzy cognitive map model based on relational mapping to obtain the probability distribution results of operational data; Design an efficient anomaly detection algorithm for operational data through big data information fusion and association rule mining methods. The test results show that this method can improve the sensitivity and detection rate of abnormal data detection, reduce false detection rate, KL divergence, and average detection delay time. The accuracy and efficiency of abnormal detection in operational data are high.

关键词： Accuracy Sensitivity Fuzzy cognitive maps Probability distribution Data models Association rule learning Delays Internet of Things detection algorithms Anomaly detection

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Overlapping Community detection in Complex Networks Using Multi-Objective Particle Swarm Optimization Algorithm

Overlapping Community Detection in Complex Networks Using Mu...

引用

International Conference on Smart Computing & Communications (ICSCC)

作者： Dhuha Abdulhadi Abduljabbar Computer Science Department Collage of Science University of Baghdad Baghdad Iraq

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Understanding the structures and functions of networks requires the ability to identify the hidden communities within them. In most real-world networks, research has revealed that nodes can have membership in more than one community, demonstrating the existence of overlapped community structures. As a result, overlap is a significant characteristic of networks, and overlapping community detection has attracted increasing attention recently. In this article, we present a multi-objective optimization algorithm to detect the overlapping topological structure of community in complex networks using the recent framework of particle swarm optimization algorithm. We have proposed two metrics to evaluate overlapping community structure that exhibit a negative correlation. These metrics are known as negative partition density (NPD) and community unfitness (CUF), to serve as optimization objectives. Additionally, our algorithm adopts a line graph representation approach, enabling the generation of overlapping graph partitions from the original interaction graph, thus allowing nodes to belong to multiple clusters. Finally, the proposed algorithm has been tested on both synthetic and real- world social networks, and its results have been compared against well-known state of the art algorithms for overlapping community detection problem. The experimental results demonstrate the effectiveness and efficiency of our proposed algorithm.

关键词： Measurement Social networking (online) Heuristic algorithms Clustering algorithms Complex networks Linear programming Partitioning algorithms Particle swarm optimization detection algorithms Optimization

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Distributed Radar Target detection with Doppler Channel Maximum Quantization

Distributed Radar Target Detection with Doppler Channel Maxi...

引用

IEEE International Conference on Radar

作者： Fabing Dou Man Zhang Shenghua Zhou Min Gong Pengfei Zhao Jian Yang Xin Sui National Key Laboratory of Radar Signal Processing Xidian University Xi’an China China Academy of Launch Vehicle Technology Beijing China

ISBN: (数字)9798350362381

ISBN: (纸本)9798350362398

In distributed radar, a local radar site may perform coherent accumulation for a better signal-to-noise (SNR) output and it is often unnecessary to transmit statistics in all Doppler channels to a fusion center (FC), if the probability that more than one target is present in a range cell is low. In this paper, we propose a low-communication-cost algorithm for distributed detection with locally coherent multi-pulse accumulation. Under the assumption that no more than one target is present in a range cell, we take the maximum of the test statistics over all Doppler channels to represent the presence of a target and then quantize the statistic maximum with the least squares quantization (LSQ) algorithm for a low communication bandwidth requirement. An optimal fusion rule based on the Neyman-Pearson (NP) criterion is formulated to make a global decision under a constant false alarm rate. Numerical results indicate that compared with traditional algorithm, the proposed algorithm with 3-bit LSQ quantization results in less than 0.5 dB SNR loss whereas the communication bandwidth is reduced by 99.71%.

关键词： Quantization (signal) Data preprocessing Radar detection Bandwidth Object detection Probability Doppler radar Doppler effect detection algorithms Signal to noise ratio

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A Forest Harmful Plant Goldenrod detection Method Based on Enhanced YOLOv5

A Forest Harmful Plant Goldenrod Detection Method Based on E...

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Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Hengyu Ma Bin Fang Yuting Zhang Tianzhu Qu Fuming Peng Xiaoyu Dai School of Automation Nanjing University of Science and Technology Nanjing China Automation Research Institute.NJCJ Ltd. Nanjing China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

The challenge of removing detrimental forest weeds like goldenrods remains unresolved, with weed detection playing a pivotal role in solving this issue. Factors such as irregular light distribution in wooded areas and the variety of weed characteristics frequently result in reduced precision during weed detection. To address these issues, this paper proposes a deep learning weed detection algorithm based on enhanced YOLOv5. Specifically, more accurate anchor boxes are obtained using the latest clustering algorithm K-means||, the location error of bounding boxes is reduced by improving the SIoU loss function, and the model's anti-interference capability is enhanced by incorporating the squeeze-and-excitation network (SENet) mechanism in the data preprocessing stage. Experimental results show that the improved algorithm outperforms the original YOLOv5 algorithm with an increase of 10.1%, 12.8%, and 11.2% in mean average precision (mAP), precision, and recall, respectively. Moreover, the utilization of the enhanced algorithm in weed detection within the project's demonstration forest yielded a detection accuracy of 99%, validating the effectiveness and superiority of the algorithm.

关键词： YOLO Training Deep learning Accuracy Data preprocessing Clustering algorithms Lighting Forestry Image preprocessing detection algorithms

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Research on obstacle detection algorithm based on object recognition

Research on obstacle detection algorithm based on object rec...

引用

Electrical, Automation and Computer Engineering (ICEACE), International Conference on

作者： Lu Bian Weimin Xia Yuanpeng Tan Miao Zhao State Grid Corporation Limited Beijing China State Grid Zhongxing Property Beijing Co. LTD Beijing China China Electric Power Research Institute Limited Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper delves into an obstacle detection algorithm that utilizes object recognition to enhance the precision of identifying small objects. By extending the YOLOv5s model with an additional layer dedicated to detecting small targets, we achieve multi-scale detection capabilities, thereby bolstering the recognition prowess for small obstacle targets. At the same time, the deformable convolution DCNv2 is combined to make full use of its ability to capture the features of small targets with large shape changes. To address the issue of redundant bounding boxes, we incorporate the Soft-NMS algorithm, which proficiently mitigates the inadvertent removal of bounding boxes, thereby enhancing both the accuracy and trustworthiness of the detection outcomes. Experimental findings underscore the remarkable performance improvement of the proposed algorithm, particularly in tasks involving the detection of small targets.

关键词： YOLO Automation Accuracy Target recognition Shape Convolution detection algorithms

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Power Quality Disturbance detection Method Based on Improved Adaptive S-Transform

Power Quality Disturbance Detection Method Based on Improved...

引用

Electrical Engineering and Control Science (IC2ECS), International Conference on

作者： Junming Wei Shixuan Lü Wei Wang Lijun Zheng Xin Liu Runze Hu Wenqi Wang Taiyuan University of Technology Taiyuan China State Grid Shanxi Electric Power Company Electric Power Science Research Institute Taiyuan China

ISBN: (数字)9798331510107

ISBN: (纸本)9798331510114

Aiming at the problems of poor real-time and low time-frequency resolution of the composite power quality disturbance detection algorithm, a power quality disturbance detection method based on improved adaptive S-transform (IAST) is proposed. A global adaptive Gaussian window is constructed as the kernel function of IAST, which can adaptively adjust the effective window length and spectrum of the window with the change of the detection frequency, avoiding the frequent switching of window parameters to improve time-frequency resolution and reducing algorithm complexity. Window parameters are selected with the objective of enhancing signal energy concentration, ensuring accurate time-frequency positioning of various disturbances. An automatic threshold method is used to determine the main frequency point of the actual disturbance signal and performing time-frequency transformation on these points, further improving the execution efficiency of algorithm. Simulation and measurement results show that compared with existing composite power quality disturbance detection algorithms has good real-time performance and time-frequency resolution capabilities, low computational complexity, and is suitable for real-time and accurate detection of complex power quality disturbances.

关键词： Time-frequency analysis Accuracy Power measurement Power quality Switches Real-time systems detection algorithms Kernel Signal resolution Tuning

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Optimized and Improved YOLOv8 Dense Pedestrian detection Algorithm

Optimized and Improved YOLOv8 Dense Pedestrian Detection Alg...

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Communication, Image and Signal Processing (CCISP), International Conference on

作者： Jingkun Mao Haoyu Wang Deheng Li Electrical Engineering and automation Tianjin University of Technology Tianjing China Futu-re co. Nagoya Japan

ISBN: (数字)9798350356656

ISBN: (纸本)9798350356663

In large, densely populated scenarios, abnormal human gatherings are common, and due to the presence of many small-scale occluded pedestrians, missed detections are likely. To tackle this challenge, an improved multi-scale occluded pedestrian detection algorithm based on YOLOv8n is proposed. Firstly, to address the large variation in the scale of occluded pedestrians, the algorithm introduces the lightweight network RepViT to enhance multi-scale features. Next, the GAM attention module is added to improve the representation of multi-scale images and positional information. Finally, to improve the recognition precision for minor targets that are often missed, a small object detection head is introduced, using Inner-WIoUv3 as the loss function to enhance the model's convergence capability. Experimental results show that the improved model achieves enhancements on the public dense pedestrian datasets WiderPerson and CrowdHuman, with mAP50 increasing by 1.8% and 5.1%, and recall rates increasing by 2.2% and 5.5%, respectively. The improved algorithm shows a higher level of detection precision when compared with the baseline model.

关键词： Pedestrians Head Target recognition Signal processing algorithms Object detection Signal processing Feature extraction detection algorithms Convergence

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Stochastic Gradient Descent Based MIMO detection with Interference Rejection Combining

Stochastic Gradient Descent Based MIMO Detection with Interf...

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International Conference on Communication Technology (ICCT)

作者： Yubin Zhu Ruobing Yang Ya Gao Kaining Han Jianhao Hu National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Multiple-input multiple-output (MIMO) technology promises high spectrum utilization and system capacity and has been seen as a key technology for modern wireless communication systems. With the rapid increase in the demand for system capacity, the interference between adjacent cells cannot be ignored, and the antenna scale is constantly increasing, which makes it increasingly difficult for uplink MIMO receivers to achieve the cubic complexity of the MMSE-IRC algorithm. Some methods based on series or approximate solutions provide quadratic complexity but have difficulty achieving interference suppression. To solve this issue, we proposed a method for calculating the equalization matrix based on the stochastic gradient descent algorithm. We theoretically proved that it can converge to the equalization matrix of the MMSE-IRC algorithm. After that, we experimentally demonstrated that its performance loss is within 0.2dB and the complexity does not exceed the quadratic, which provides a potential solution for reducing the complexity of uplink receivers in massive MIMO systems.

关键词： Interference suppression Receiving antennas Massive MIMO Approximation algorithms Hardware Complexity theory Uplink detection algorithms

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