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检索条件"主题词=Detection Algorithms"

共 7380 条记录，以下是921-930 订阅

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Faking Fluent: Unveiling the Achilles’ Heel of Multilingual Deepfake detection

Faking Fluent: Unveiling the Achilles’ Heel of Multilingual...

引用

IEEE International Joint Conference on Biometrics (IJCB)

作者： Rishabh Ranjan Bikash Dutta Mayank Vatsa Richa Singh Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

With the rapid advancement of deep learning techniques, the generation of audio deepfakes has achieved remarkable realism across various languages and accents. However, the effectiveness of audio deepfake detection models in diverse linguistic environments remains a crucial area of investigation. This paper presents the first empirical study on the robustness of current audio deepfake detection algorithms across different languages and accents. We evaluate whether these models maintain their effectiveness across varied linguistic domains or perform better in specific language contexts. Our comprehensive analysis examines state-of-the-art audio deepfake detection models trained on the ASVspoof 2019 and BhashaBluff datasets, assessing their performance across four diverse datasets: three representing similar-language variations (Speech Accent Archive, Svarah, and the UK English Accent Dataset) and one representing a different language (Vaani). Our results and supporting analysis indicate that while current models perform well on benchmark datasets, their ability to generalize across diverse linguistic conditions is limited. We identify potential vulnerabilities in existing models when faced with unfamiliar languages or accents, highlighting the need for more inclusive and adaptable detection systems. Our results highlight the need to enhance the robustness of audio deepfake detection across the global linguistic spectrum and emphasize the importance of developing models capable of effectively identifying synthetic speech, regardless of language or accent.

关键词： Deep learning Deepfakes Analytical models Adaptation models Visualization Phonetics Robustness Security Speech synthesis detection algorithms

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DCAPT-Net: A Multi-Scale Ship detection Algorithm for SAR Images

DCAPT-Net: A Multi-Scale Ship Detection Algorithm for SAR Im...

引用

International Conference on Big Data and Information Analytics (BigDIA)

作者： Haosheng Song Lin Wang Luran Mi Hua Yu Jingwei Gan The 54th Research Institute of CETC Shijiazhuang China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Within the recent timeframe, the detection of ships in SAR images has become a critical task for many countries. To tackle the issue of multi-scale ship detection, this study presents DCAPT-Net, a new ship detection algorithm. This algorithm enhances multi-level feature fusion by introducing a novel dense connection attention pyramid structure. Furthermore, it incorporates a dedicated small object detection layer and a four-head Transformer structure detection head to improve the representation of small targets. Collectively, these innovations achieve effective multi-scale ship detection. Studies performed using the High Resolution SAR Images Dataset (HRSID) and the SAR Ship detection Dataset (SSDD) highlight the DCAPT -Net's superior performance in identifying ship targets. Additionally, ablation studies confirm the efficacy of the network improvements introduced.

关键词： Technological innovation Head Image resolution Object detection Big Data Transformers Radar polarimetry Marine vehicles detection algorithms

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Network Topology detection Method Based on BGP Protocol and Simulation Verification

Network Topology Detection Method Based on BGP Protocol and ...

引用

International Conference on Big Data and Information Analytics (BigDIA)

作者： Rui Xuan Fei Wang Shuhui Chen the School of Computer (of National University of Defense Technology) Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

With the development of network technology, the network topology structure becomes more and more complex. The AS-level topology in the Internet reflects the logical connection relationship among the autonomous systems in the network, and is the foundation of the Internet of Things management, network detection and traffic monitoring. The mainstream AS-level topology identification method at home and abroad is based on the BGP path AS path attribute information to detect the connection relationship between AS and achieve network topology restoration. In this paper, a topology detection method based on the traffic information is designed, and the establishment of network environment and the simulation verification of network topology detection method are realized based on the EVE-NG simulation platform.

关键词： Protocols Network topology Telecommunication traffic Big Data Routing Real-time systems Topology Internet of Things detection algorithms Monitoring

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Lightweight Tea Shoots detection Algorithm Based on Deep Learning

Lightweight Tea Shoots Detection Algorithm Based on Deep Lea...

引用

Robotics, Control and Automation Engineering (RCAE), International Conference on

作者： Haoran Wu Jingui Zhang Yongjun Yang Daoxiong Gong Faulty of Information Technology Beijing University of Technology Beijing China Liangshitang Sub-District Office Agricultural Comprehensive Service Center Shaoyang China Hunan Tea Research Institute Hunan Academy of Agricultural Sciences Changsha China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

In machine tea picking, the complex tea garden environment and frequent occlusions require an algorithm that can accurately identify tea shoots while being deployable on resource-limited mobile devices. To address this, a lightweight YOLO-TD (You Only Look Once-Tea Detect) tea shoots detection algorithm based on improved YOLOv8 is proposed. The algorithm uses lightweight C2f-faster modules for feature extraction, reducing model complexity while enhancing central feature extraction capabilities. Inner-SIoU is employed as the loss function to optimize training and improve sensitivity to small tea shoots. Additionally, the G-Head structure with a dual-branch feature extraction design is used. Experimental results show that the proposed algorithm achieves a mean average precision (mAP) of 82.20%, reduces the Giga Floating-point Operations Per Second (GFLOPS) by 45.24%, accurately identifies key points of tea shoots, and effectively detects occluded tea shoots. This improved algorithm offers a robust solution for tea shoot detection in machine tea picking.

关键词： Location awareness Training Automation Sensitivity Feature extraction Mobile handsets Magnetic heads Object recognition detection algorithms Robots

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T-YOLOv5: A Traffic Sign detection Algorithm Based on Multilayer Feature Fusion

T-YOLOv5: A Traffic Sign Detection Algorithm Based on Multil...

引用

IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Yuechong Zhang Qiaomei Zhang Jinhang Tian Dapeng Liu Haiying Liu Lixia Deng School of Information and Automation Engineering Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Wuzhou Electric Co. Ltd Weifang China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In recent years, assisted driving has become a hot research topic, and traffic sign detection, as an essential part of assisted driving, has also received great attention. This article addresses the issues of lighting impact and distance between traffic signs and detection equipment in the field of traffic sign detection. A feature extraction module based on multi-layer feature semantic fusion is proposed to improve the algorithm's ability to extract deep features while retaining shallow semantic features and positional information. Through experimental verification, the proposed T-YOLOv5 algorithm has improved by 5.1% compared to the original algorithm in mAP0.5, with good performance improvement.

关键词： YOLO Measurement Location awareness Hands Semantics Lighting Feature extraction Nonhomogeneous media Data mining detection algorithms

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Community detection Algorithm based on Graph Capsule Networks

Community Detection Algorithm based on Graph Capsule Network...

引用

Image Processing (ASIP), Asia Symposium on

作者： Ran Yang Xiaoyan Zheng Yiwen Shen Zhiya Zhou School of Information Technology and Engineering Tianjin University of Technology and Education Tianjin China Qingxian No.1 Middle School in Cangzhou City Information Technology Engineering Association China

ISBN: (数字)9798350376159

ISBN: (纸本)9798350376166

GNN (Graph Neural Network) is a type of neural network model specifically designed to handle graph-structured data. Compared to traditional neural network models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNN aims to process data with nodes and edges to capture the relationships and structure among nodes in a graph. However, existing GNNs have limited capabilities in obtaining hierarchical graph representations, which are crucial for graph classification. To address the limitations of GNNs, this paper proposes an improved graph capsule network that incorporates a data disentanglement module to recover the underlying independent factors from observed mixed data, thus unraveling the heterogeneous factors behind node embeddings. By extracting node features in the form of capsules and utilizing routing mechanisms, important information at the graph level can be captured. This allows for the decomposition of the feature representation of each node into independent representations corresponding to different factors or attributes, enabling a better understanding and utilization of the diverse features of nodes and enhancing the effectiveness and capabilities of graph analysis and learning.

关键词： Recurrent neural networks Asia Routing Feature extraction Graph neural networks Data models Data mining Convolutional neural networks detection algorithms

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Bad Data detection Algorithm for Power System Based on K-Prototypes Clustering and Broad learning

Bad Data Detection Algorithm for Power System Based on K-Pro...

引用

Intelligent Power and Systems (ICIPS), International Conference on

作者： Junye Li Tiantian Cai Jie Shao China Southern Power Grid Digital Grid Research Institute Co. Ltd. Guangzhou China

ISBN: (数字)9798331532765

ISBN: (纸本)9798331532772

In order to solve the problem of low identification rate of bad data in power grid state estimation, this paper proposes a bad data detection method based on K-prototypes clustering and broad learning. Firstly, the K-prototypes clustering algorithm is improved by increasing the adaptive part of the algorithm and selecting the best class. Secondly, the broad learning algorithm is used to compare the predicted quantity and the modified quantity measurement of state estimation, and the two algorithms are organically combined through the algorithm joint parameters to screen out bad data and improve the recognition rate and operation speed. Finally, through the simulation analysis of IEEE-30 bus case, the results show that compared with common detection methods, the proposed algorithm can effectively improve the identification rate and operation speed of bad data.

关键词： Deep learning Accuracy Clustering algorithms Prediction algorithms Power grids Hybrid power systems State estimation detection algorithms Long short term memory

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Infrared Small Target detection Based on DETR Architecture and Super-Resolution Technique

Infrared Small Target Detection Based on DETR Architecture a...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Huanyu Yang Lijun Yang Jun Wang Yuming Bo Jiacun Wang College of Automation Nanjing Univ. of Sci. and Tech Nanjing China Dept. of Comp. Sci. and Softw. Eng. Monmouth University USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Infrared small target detection (ISTD) holds significant importance in domains such as maritime search and rescue, and autonomous driving. To enhance the detection capabilities of infrared small targets against complex backgrounds, a novel detection algorithm based on an improved detection Transformer (DTER) is proposed. This algorithm leverages the DTER detection framework and the EDSR network, utilizing super-resolution reconstructed images as inputs. It incorporates the Enhanced Multi-Scale Attention (EMA) module and an improved backbone structure. Moreover, it employs a micro-target detection encoder head with a new feature layer S2 to elevate the quality of minute feature extraction. The proposed method achieved a mAP@50 of 96% and mAP@(50:95) of 54.6% on a public dataset. Compared to current state-of-the-art methods for infrared small target detection, it demonstrates superior capabilities in reducing false positives and misses while maintaining commendable real-time performance.

关键词： Head Accuracy Superresolution Object detection Feature extraction Transformers Real-time systems detection algorithms Image reconstruction Remote sensing

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Apple Leaf Disease detection Algorithm Based on an Improved YOLOv8

Apple Leaf Disease Detection Algorithm Based on an Improved ...

引用

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

作者： Xin Zhao Qi Ye Mingtao Ma Lifen Wang Jilin Institute of Chemical Technology Jilin China JiLin Agricultural Science and Technology University Jilin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

To improve the precision of recognizing apple leaf diseases in real-world applications, a detection model for apple leaf diseases is proposed, utilizing an enhanced YOLOv8 architecture. A Convolutional Block Attention Module (CBAM), incorporating both channel and spatial attention mechanisms, is introduced to enhance the extraction of disease-specific features while effectively ignoring irrelevant information in the images. A DyHead detection head replaces the original head in the YOLOv8 model, integrating scale, spatial, and task attention mechanisms to further enhance the model's detection performance. Compared with the original YOLOv8 algorithm, the improved YOLOv8 model increases mAP@0.5 by 1.6% and mAP@0.5:0.95 by 1.2%. Experimental results demonstrate that the proposed improved YOLOv8 algorithm achieves high recognition accuracy and detection speed, providing a precise real-time detection method for apple leaf diseases.

关键词： Head Attention mechanisms Accuracy Computational modeling Object detection Feature extraction Magnetic heads Real-time systems detection algorithms Diseases

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Traffic Sign detection Algorithm Based on Improved YOLOv5

Traffic Sign Detection Algorithm Based on Improved YOLOv5

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International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Jingwen Sheng Donghui Shi Penglin Wang Xiaolan Gong School of Electronics and Information Engineering Anhui Jianzhu University Hefei China Anhui Province Key Laboratory of Intelligent Building & Building Energy Saving Hefei China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

Recently, driverless technology has developed rapidly, and improving the accuracy and speed of traffic sign detection is of great significance to reduce the frequency of traffic accidents and maintain the safety of people's lives and property. In order to solve the problem of low detection accuracy caused by small size, high density and complex background environment of traffic signs in actual production and life, an optimized YOLOv5s algorithm was proposed. First, SimAM was embedded in the Head part of YOLOv5s to adjust the attention weight, making the model more focused on important information and reducing the omission of small targets to a certain extent. Secondly, Focal GIoU was used to solve the problem of sample difficulty and class imbalance in target detection, so that the model could identify and locate the target more accurately. Finally, Mish was used as the activation function during model training, which improved the generalization ability and network optimization ability of the model. The experimental results show that the mAP.5 and mAP.5:.95 of the improved algorithm reach 93.8% and 87.8%, respectively. Compared with the base model, the improvement is increased by 3.4% and 3.5% respectively.

关键词： YOLO Training Accuracy Computational modeling Production Safety detection algorithms Optimization Accidents

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