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检索条件"主题词=Detection Algorithms"

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An Efficient Fire detection Algorithm with Dataset Generation Based on Diffusion

An Efficient Fire Detection Algorithm with Dataset Generatio...

引用

Chinese Automation Congress (CAC)

作者： Gaoyang Wang Hongtao Zheng Duo Xiao College of Control science and Engineering Zhejiang University Hangzhou China School of Information & Electrical Engineering Hangzhou City University Hangzhou China The academy of Edge Intelligence Hangzhou City University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Faced with increasing urbanization and population growth, the hazard of fires has become a critical issue in urban areas worldwide. Therefore, the focus on how to achieve accurate and rapid fire detection using artificial intelligence has intensified. Currently, fire detection algorithms often struggle to balance precision and processing speed. At the same time, high-quality fire detection datasets are extremely scarce, severely limiting the performance of algorithms. To address these challenges, this paper first proposes an enhanced fire detection algorithm framework based on YOLOv8, aiming to comprehensively improve the accuracy and processing speed of detection. This framework integrates Swin Transformer and Slim Neck to enhance model performance, and utilizes WIoUv3 for network training regularization. Additionally, this paper introduces a novel fire detection dataset generation algorithm: Fire-Generator, to address the scarcity of high-quality training data. This tool utilizes a latent diffusion model to guide image generation, enabling the synthesis of training data from textual descriptions or existing images. Experimental evaluations demonstrate that our method outperforms most currently available fire detection algorithms in terms of performance. The versatility of Fire-Generator extends beyond fire detection, demonstrating potential for generating training data across different domains. Through this comprehensive strategy, our model achieves significant results by leveraging both real and synthesized data.

关键词： Training Accuracy Limiting Image synthesis Urban areas Training data Fires Transformers Neck detection algorithms

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Deep Learning-Based DDoS Attack detection Using Adversarial Optimization

Deep Learning-Based DDoS Attack Detection Using Adversarial ...

引用

IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Dahai Yu Jianming Cui Yungang Jia Peiguo Fu Ming Liu School of Information Engineering Chang’an University Xi’an China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

In the field of cybersecurity, increasingly emerging network threats seriously affect the security of network and users. Notably, DDoS attacks represent a critical concern due to their capacity to inundate and debilitate targeted systems via distributed networks. This study introduces a DDoS attack detection framework employing a dual-detection model designed to identify both conventional and adversarial DDoS attacks. Adversarial DDoS attacks are characterized by their enhanced capability to circumvent standard DDoS detection mechanisms. The proposed framework incorporates an optimized CNN architecture for the detection of traditional DDoS attacks. This optimization involves substituting fully connected layers with global average pooling layers, an approach validated through comparative experimentation to yield the most effective base CNN model. For the detection of adversarial DDoS attacks, we propose a new detection algorithm, in which methodology initially employs the WGAN-GP data generation process to create synthetic DDoS attack data, followed by the strategic alteration of certain feature values within the generated attack data using normal traffic data. The resultant perturbed data are then utilized to train the adversarial DDoS attack detection model. Empirical assessments performed on the CIC-IDS2017 and CIC-DDoS2019 datasets confirm the efficacy of the dual-detection model in accurately identifying both traditional and adversarial DDoS attacks, achieving an accuracy exceeding 95%.

关键词： Accuracy Denial-of-service attack Generative adversarial networks Feature extraction Real-time systems Data models Convolutional neural networks Computer crime detection algorithms Optimization

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A Speed detection Method for BLDC Motor

A Speed Detection Method for BLDC Motor

引用

Information Technology and Data Science (CITDS), IEEE Conference on

作者： Fehmi Sevilmis Ali Sait Özer Hulusi Karaca Dept. of Electrical and Electronics Engineering Selçuk University Konya Türkiye Dept. of Control and Automation Technology Konya Technical University Konya Turkiye

ISBN: (数字)9798350387889

ISBN: (纸本)9798350387896

It is very important to accurately determine the position and speed of BLDC motor. The use of an encoder to precisely control the motor is a suitable solution, but the placement of the encoder on the motor shaft causes the volume of the system to increase. In addition, the high cost of the encoder is another disadvantage. To overcome these problems, sensorless control algorithms can be used for position and speed detection. However, the complex structure of sensorless control methods and their inadequate performance at low speeds cause them not to be preferred in practical applications. For these reasons, hall-effect sensor control algorithms come to the forefront due to their simple structure and low cost. Since the resolution of hall sensor signals is low, these signals must be processed properly in speed detection. For this purpose, MAF-PLL-based speed detection method is proposed in this study. The proposed method is tested under different conditions. The results obtained show the accuracy of the proposed speed detection algorithm.

关键词： Shafts Costs Rotors Estimation Sensorless control Data science Motors detection algorithms Information technology Signal resolution

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CrossAlert: Enhancing Multi-Stage Attack detection through Semantic Embedding of Alerts Across Targeted Domains

CrossAlert: Enhancing Multi-Stage Attack Detection through S...

引用

IEEE Conference on Communications and Network Security (CNS)

作者： Nadia Niknami Vahid Mahzoon Jie Wu Center for Networked Computing Temple University USA

ISBN: (数字)9798350375961

ISBN: (纸本)9798350375978

With the continuous evolution of attack methods, cyber attacks have become more distributed and complex, employing techniques such as multi-stage network attacks (MSA). Monitoring tools generate numerous alerts during these attacks, but the high dimensionality and diverse features of alert data often result in poor detection performance. Manual analysis of MSAs is time-consuming, leading to limited labeled data. Additionally, changes in attack types and new domains cause Intrusion detection Systems (IDSs) to perform poorly, presenting a significant challenge known as domain shift. In this paper, we address these issues by proposing a multi-stage network attack detection algorithm that enhances MSA detection through the analysis of high-dimensional alerts and the integration of various alert aspects. Our algorithm incorporates multiple facets including semantic similarity, anomaly scores, and feature extraction to identify relevant entities, detect intricate relationships, and uncover hidden patterns, enhancing the detection of multi-stage network attacks. The model was tested successfully using the DARPA 2000 and ISCX 2012 datasets, with completeness and soundness measured to evaluate its effectiveness.

关键词： Scalability Semantics Intrusion detection Manuals Network security Feature extraction Reliability detection algorithms Monitoring Cyberattack

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Community detection for Large Graphs on GPUs with Unified Memory

Community Detection for Large Graphs on GPUs with Unified Me...

引用

IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Emre Dinçer Iŝil Öz Computer Engineering Izmir Institute of Technology Izmir Turkey

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

While GPUs accelerate applications from different domains with different characteristics, processing large datasets gets infeasible on target systems with limited device memory. Unified memory support makes it possible to work with data larger than available GPU memory. However, page migration overhead for executions with irregular memory access patterns, like graph processing workloads, induces severe performance degradation. While memory hints help to deal with page movements by keeping data in suitable memory spaces, coarse-grain configurations can still not avoid migrations for executions having diverse data structures. In this work, we target the state-of-the-art CUDA implementation of the Louvain community detection algorithm and evaluate the impacts of the fine-grained unified memory hints on the performance. Our experimental evaluation shows that memory hints configured for specific data structures reveal significant performance improvements and enable us to work efficiently with large graphs.

关键词： Performance evaluation Degradation Codes Runtime Memory management Graphics processing units Data structures detection algorithms

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DFT-Net: A Bimodal Object detection Algorithm for Complex Traffic Environments

DFT-Net: A Bimodal Object Detection Algorithm for Complex Tr...

引用

IEEE International Conference on Industrial Informatics (INDIN)

作者： Jing Lian Yibin Zhang Haoyu Li Jun Hu Linhui Li School of Mechanical Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

In response to the challenge of single-modal sensors struggling to rapidly and accurately detect in complex traffic environments, a DFT-Net detection algorithm based on the fusion of visible light and infrared bimodal features is proposed. Firstly, the algorithm constructs a dual modal feature extraction network and designs an efficient aggregation module with stronger feature extraction capability. Secondly, the algorithm utilizes a framework of self-attention and cross-attention mechanisms to fuse the features of visible light and infrared modalities. This framework facilitates information interaction between different modalities through query-guided mechanisms. To reduce the computational load of the dual modal feature transformer module, a spatial feature compression module is introduced. Finally, in order to mitigate visible noise and low infrared resolution, this paper combines Transformer and CBAM attention mechanism. The designed algorithm is compared with various detection algorithms on multiple datasets, and the results demonstrate its effectiveness. On the FLIR-gan dataset, compared to the YOLOv7 detection algorithms for visible light and infrared, our DFT-Net achieved an accuracy improvement of 26.8% and 26.7% respectively, and 2.8% improvement compared to the bimodal detection algorithm CFT. This indicates that the algorithm exhibits good detection performance in complex traffic environments.

关键词： Fuses Noise Object detection Sensor phenomena and characterization Sensor fusion Feature extraction Transformers detection algorithms Spatial resolution Informatics

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Road Crack detection Algorithm Based on Large Convolution Kernels

Road Crack Detection Algorithm Based on Large Convolution Ke...

引用

International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Yunxiang Liu Bin Nan Shanghai Institute of Technology Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Road crack detection is widely used in road maintenance and bridge safety monitoring. The complex textures of road cracks lead to occlusion between targets, and environmental factors affect images, causing color shifts and blurring. To address these issues, this paper proposes the YOLOv8-LSK road crack detection algorithm. First, a Spatial Fusion Module (SMF) is designed to enhance the focus on spatial dimension information, improving the detection capability of occluded cracks. Second, a large-kernel convolution attention feature extraction module is designed to enhance the feature extraction capability of blurred and color-shifted road images. Finally, structural reparameterization (SR) is applied to the cross-scale feature fusion unit of YOLOv8-LSK, optimizing the model’s detection speed. Experimental results show that the YOLOv8-LSK algorithm achieves a detection accuracy of $\mathbf{7 2. 1 \%}$ on the RDD2022 dataset, with detection speeds of 33FPS, respectively.

关键词： Convolution Image color analysis Roads Feature extraction Safety Maintenance detection algorithms Kernel Informatics Monitoring

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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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Road Trash detection Algorithm of RT-DETR Integrated with State Space

Road Trash Detection Algorithm of RT-DETR Integrated with St...

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

作者： Zexing Luo Guoqing Yang Xinyong Zhao Yeqin Shao School of Transportation and Civil Engineering Nantong University Nantong China Suzhou Research Institute of Industrial Technology Zhejiang University Hangzhou China Hualuyiyun Technology Co. Ltd NanJing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

As global urbanization progresses rapidly, the issue of urban road trash is becoming increasingly severe. Traditional manual cleaning methods are inefficient and struggle to meet the growing demand. This paper proposes an improved road trash detection algorithm based on RT-DETR (Real-Time detection Transformer) and State Space Model (SSM) to enhance detection accuracy and robustness. The algorithm incorporates an innovative feature pyramid structure and a state-space-based internal scale feature enhancement module, effectively addressing object detection in complex scenes as well as challenges associated with occlusions and small object detection. We constructed a diverse urban road trash detection dataset comprising over 60,000 images, providing a solid foundation for the training and testing of the algorithm. Experimental results show that the proposed model improves the mean average precision (mAP) on the COCO dataset by 0.8 percentage points and achieves a 1.2 percentage point improvement on our custom road trash detection dataset, with a 2.1 percentage point increase in the precision of small object detection. The proposed algorithm achieves a favorable balance between real-time performance and accuracy, significantly enhancing trash detection in complex environments, particularly in terms of small object recognition and detection accuracy under occlusion conditions.

关键词： Accuracy Roads Computational modeling Urban areas Object detection Feature extraction Robustness Real-time systems Object recognition detection algorithms

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A Change detection Algorithm for High Resolution Remote Sensing Images based on Data Analysis

A Change Detection Algorithm for High Resolution Remote Sens...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Tao Liu Bohan Zhang College of Computer Science and Technology Heilongjiang Institute of Technology Harbin China School of Information Engineering China University of Geosciences Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

With the development of remote sensing technology to high resolution, high resolution remote sensing images are widely used in change detection. This paper uses Convolutional Neural Network (CNN) as its core model. The first step is data preprocessing, including data cutting, re-projection and normalization. Then the output of post-processing and change model is refined by threshold processing and morphological operation. Experiments show that the maximum detection time of CNN is only 400 milliseconds, showing higher speed performance. In terms of detection accuracy, the best CNN accuracy rate is 96.6%, and the worst CNN accuracy rate is 72.3%. The highest accuracy rates of RNN and SVM are 69.4% and 67.3%, respectively. As far as the reliability of detection is concerned, CNN has the best reliability, which is 96.9%, RNN is 79% and SVM is 72.2%. These results show that CNN can achieve better performance in typical high-resolution remote sensing image change detection tasks.

关键词： Support vector machines Accuracy Data analysis Change detection algorithms Data preprocessing Data enhancement Data models Convolutional neural networks detection algorithms Remote sensing

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