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检索条件"任意字段=Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition"

共 786 条记录，以下是11-20 订阅

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thangka Image Object Detection Method Based on Improved YOLOv8 24

Thangka Image Object Detection Method Based on Improved YOLO...

引用

13th international conference on computing and pattern recognition, ICCPR 2024

作者： Xian, Yukai Zhao, Qijun Yang, Xin Gao, Dingguo School of Information Science and Technology Tibet University Lhasa Tibet China

ISBN: (纸本)9798400717482

this paper explores object detection techniques in thangka images using an improved YOLOv8 algorithm. To address challenges such as unpredictable occlusions and damages, significant variations in artistic styles, and the difficulty of detecting small objects in thangka images, this study proposes an enhanced YOLOv8 algorithm. By introducing the Focus Self-Attention mechanism, the algorithm effectively captures long-range dependencies within the images. the paper also introduces the HAFM module, which enhances the model's spatial and channel awareness. the experiments, conducted on a dataset of 3,747 thangka images, show that the improved algorithm achieves mAP@0.5 and mAP@0.5:0.95 scores of 91.5% and 53.5%, respectively, significantly outperforming traditional methods. the findings demonstrate that the enhanced YOLOv8 algorithm can more accurately identify various objects in thangka images, offering valuable technical support for the digital preservation and development of thangka art. © 2024 Copyright held by the owner/author(s).

关键词： thangka

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High-Speed Object Detection in Near Space Based on Improved YOLOv8s 24

High-Speed Object Detection in Near Space Based on Improved ...

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13th international conference on computing and pattern recognition, ICCPR 2024

作者： Ma, Ming Hu, BoTao Song, QingZeng School of Life Sciences Tiangong University Tianjin China School of Computer Science and Technology Tiangong University Tianjin China

ISBN: (纸本)9798400717482

Aiming at the problem of low accuracy and weak robustness of small target detection in high-speed near-air environment of traditional network models, a CDGH-YOLO algorithm model based on YOLOv8s optimization and improvement is proposed. the model reduces the number of parameters and the amount of calculation by integrating lightweight convolution modules (Ghost convolution and DW convolution);at the same time, the recursive gated convolution module (C3HG) is adopted to greatly improve the model's detection ability for small objects;in addition, by adding the input-output attention module (CBAM), the efficiency and robustness of the overall small target feature recognition of the model in special scenarios are improved;finally, by replacing the full intersection-over-union (CIoU) loss function with the WIoU v3 (Wise-IoU version 3) loss function, more attention is paid to the normal quality anchor box of small and medium-sized targets, thereby improving the detection efficiency of small samples. Experimental results show that the CDGH-YOLO algorithm model is superior to the baseline model YOLOv8s in terms of target recognition accuracy, recall rate, and mAP value. the versatility of the model was tested using the NWPU VHR-10 dataset. the results showed that the target recognition accuracy increased by 9.5 percentage points, and the recall rate and mAP@0.5 increased by 2.6 and 2.7 percentage points respectively. these performance indicators are significantly better than the comparison algorithms, meet the requirements of edge computing devices, and have practical application value. © 2024 Copyright held by the owner/author(s).

关键词： Convolution

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Research on Small Object Detection Algorithm Based on YOLOv8 24

Research on Small Object Detection Algorithm Based on YOLOv8

引用

8th international conference on Computer Science and Artificial Intelligence, CSAI 2024

作者： Zhang, Haojin Shi, Wei School of Information Engineering Ningxia University Ningxia Yinchuan China

ISBN: (纸本)9798400718182

In recent years, with the rapid development of deep learning technology, small object detection methods based on deep learning have been widely studied, and deep learning algorithms can achieve good results in small object detection. For small targets in images, existing detection methods still suffer from weak feature expression ability and low detection performance. In order to improve the detection ability of small targets, this paper improves the YOLOv8 algorithm. Firstly, the BiFPN network structure is introduced in the Neck module of YOLOv8 to alleviate the problem of insufficient feature information of small targets. then, a Coordinate Attention mechanism is added to the backbone network to focus on key features. then, a P2 detection head is added to the Head module of the YOLOv8 network, effectively improving the serious problem of small target missed detection. Finally, in the improved loss function module, this paper adopts WiseIoU Loss to solve the problem of slow convergence speed and low efficiency of the model, further improving the overall performance of the model. After a series of experiments, the results show that the improved algorithm in this paper can effectively improve the network’s ability to detect small targets. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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Research on the Detection Methods for Changes in the Mangroves of Dongzhaigang, Hainan from 2019 to 2023 24

Research on the Detection Methods for Changes in the Mangrov...

引用

8th international conference on Electronic Information Technology and Computer Engineering, EITCE 2024

作者： Zhang, Xianyao Lu, Huajun Qiu, Shuailong Liu, Lijin Wu, Zhongqiang School of Information Science and Technology Hainan Normal University Hainan Haikou China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400710094

this study introduces the research methods for detecting changes in the mangroves of Dongzhaigang, Hainan from 2019 to 2023. Mangrove ecosystems play a crucial role in providing habitats and have ecological and economic value, but in recent years, they have faced degradation and management issues. In order to effectively monitor mangrove changes, researchers have utilized remote sensing techniques and machine learning methods for mangrove cover information extraction and classification. the paper describes the methods of estimating mangrove cover using remote sensing satellite imagery and spectral indices and explores the applications of clustering, decision trees, support vector machines, and deep learning in mangrove change detection. the research results indicate that the support vector machine algorithm performs well in classifying mangrove and non-mangrove areas. this study provides a basis for the management and decision-making of Dongzhaigang Mangrove Nature Reserve in Hainan Province and highlights the future research potential in the application of multi-source and multi-resolution data and deep learning techniques. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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ICPR 2024 Competition on Multilingual Claim-Span Identification 27th

ICPR 2024 Competition on Multilingual Claim-Span Identifica...

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27th international conference on pattern recognition, ICPR 2024

作者： Poddar, Soham Paul, Biswajit Basu, Moumita Ghosh, Saptarshi Indian Institute of Technology Kharagpur Kharagpur721302 India Centre for Artificial Intelligence and Robotics DRDO Bangalore560093 India NSHM Institute of Computing and Analytics NSHM Knowledge Campus Kolkata700053 India

ISBN: (纸本)9783031801389

A lot of claims are made in social media posts, which may contain misinformation or fake news. Hence, it is crucial to identify claims as a first step towards claim verification. Given the huge number of social media posts, the task of identifying claims needs to be automated. this competition deals with the task of ‘Claim Span Identification’ in which, given a text, parts/spans that correspond to claims are to be identified. this task is more challenging than the traditional binary classification of text into claim or not-claim, and requires state-of-the-art methods in pattern recognition, Natural Language Processing and Machine Learning. For this competition, we used a newly developed dataset called HECSI containing about 8K posts in English and about 8K posts in Hindi with claim-spans marked by human annotators. this paper gives an overview of the competition, and the solutions developed by the participating teams. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Claim detection Claim-span identification English Hindi social media

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8th international conference on pattern recognition Applications and Methods, ICPRAM 2019

8th International Conference on Pattern Recognition Applicat...

引用

8th international conference on pattern recognition Applications and Methods, ICPRAM 2019

ISBN: (纸本)9783030400132

the proceedings contain 7 papers. the special focus in this conference is on pattern recognition Applications and Methods. the topics include: Attributes for Understanding Groups of Binary Data;annealing by Increasing Resampling;implications of Z-Normalization in the Matrix Profile;enforcing the General Planar Motion Model: Bundle Adjustment for Planar Scenes;deep Multi-biometric Fusion for Audio-Visual User Re-Identification and Verification.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602315

the proceedings contain 158 papers. the special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. the topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel Distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602933

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602247

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ICPRAM 2019 - proceedings of the 8th international conference on pattern recognition Applications and Methods

ICPRAM 2019 - Proceedings of the 8th International Conferenc...

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8th international conference on pattern recognition Applications and Methods, ICPRAM 2019

ISBN: (纸本)9789897583513

the proceedings contain 109 papers. the topics discussed include: planar motion bundle adjustment;deep learning for radar pulse detection;fast non-minimal solvers for planar motion compatible homographies;the growing n-gram algorithm: a novel approach to string clustering;pluggable drone imaging analysis framework for mob detection during open-air events;all together now! the benefits of adaptively fusing pre-trained deep representations;a cellular automata based classification algorithm;automatic information extraction from piping and instrumentation diagrams;annealing by increasing resampling in the unified view of simulated annealing;detecting and tracking surgical tools for recognizing phases of the awake brain tumor removal surgery;stochastic phase estimation and unwrapping;accelerated algorithm for computation of all prime patterns in logical analysis of data;adversarial alignment of class prediction uncertainties for domain adaptation;using recurrent neural networks for action and intention recognition of car drivers;and a fast and efficient method for solving the multiple closed curve detection problem.

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