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检索条件"主题词=Detection Algorithms"

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Research on AI-Based Target detection Technology for Sports Images

Research on AI-Based Target Detection Technology for Sports ...

引用

Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Shujian Zhang Department of Physical Education College of General Education Guangdong University of Science and Technology Dongguan China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

With the rapid development of artificial intelligence, deep learning-based target detection technology has been widely applied in various fields. Sports images, as a special application scenario, present new challenges for target detection due to their complex actions and rapidly changing targets. This paper aims to study AI-based target detection technology for sports images by analyzing and improving existing detection algorithms and proposing an optimized detection algorithm suitable for sports scenarios. First, the paper outlines the basic theories of AI and target detection technology, then analyzes the characteristics of sports images and the current application status of detection algorithms, and finally designs and implements a target detection system for sports images. Experiments validate the proposed algorithm’s effectiveness and superiority in various sports scenarios. This research provides a theoretical foundation and technical support for applications such as sports analysis, intelligent refereeing, and athlete training.

关键词： YOLO Training Performance evaluation Scientific computing Neural networks Lighting Reliability detection algorithms Artificial intelligence Sports

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Open Vocabulary SAR Object detection with Denoising and Scaled Box Loss

Open Vocabulary SAR Object Detection with Denoising and Scal...

引用

Chinese Automation Congress (CAC)

作者： Yuhao Jin Qiujin Liang Tao Zhang Jiaqi Yu Department of Automation Tsinghua University Beijing China Beijing Institute of Control and Electronic Technology

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Synthetic aperture radar (SAR) has important applications in military, geology and other fields due to its high resolution and all-day, all-weather operation. However, the inherent speckle noise of SAR images seriously restricts the effectiveness of detection algorithms. To address this challenge, this paper proposes an open vocabulary SAR object detection model that combines a denoising network and an open vocabulary detection model, named Denoise GroundingDINO (DGD). We add a denoising network AGSDNet before image encoder to remove speckle noise in SAR images and propose a new box loss function called Scaled SCA (SSCA) Loss to improve the model accuracy. Experimental results show that the DGD model has achieved excellent performance on the SSDD, HRSID and SARDet-lOOK (part) datasets, verifying the superior ability of the DGD model in the field of SAR object detection.

关键词： Vocabulary Image resolution Geology Noise reduction Noise Object detection Speckle Radar polarimetry detection algorithms Synthetic aperture radar

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Bioacoustics for North Atlantic Right Whale detection Based on Deep Learning

Bioacoustics for North Atlantic Right Whale Detection Based ...

引用

OCEANS

作者： Marc-André Blais Moulay A. Akhloufi Dept. of Computer Science Perception Robotics and Intelligent Machines Lab (PRIME) Université de Moncton Moncton NB Canada

ISBN: (数字)9798331540081

ISBN: (纸本)9798331540098

Climate change, pollution, shipping lanes and other man-made factors are significantly impacting the biodiversity of our planet. Whales represent some of the most endangered animals of our time with some species, such as the North Atlantic right whales, have fewer than 400 individuals left. These large mammals play a crucial role in maintaining the health of our planet due to their various attributes, such as their carbon sequestration abilities. Detecting, classifying and tracking these whales can provide us with tools for their management. Passive acoustic monitoring allows researchers to identify whales through audio recordings based on their unique sounds. We propose detecting North Atlantic right whales using passive acoustic monitoring audio clips. Our data consists of whale upcalls collected in the Gulf of St. Lawrence and the Gulf of Maine. In our work, we compare various audio visualization transformations such as the wavelet transformation, Short-Time Fourier Transform and a custom algorithm. Using the visualized audio, our goal is to use various deep learning algorithms to detect whales. We employed multiple pre-trained convolutional neural network algorithms, including DenseNetl21, EfficientNetB7 and Xception. the wavelet transformation achieved the best overall results with a validation accuracy of 90% on most detection algorithms. Specifically, DenseNet169 combined with the wavelet transformation achieved the highest accuracy of 90.39%. Using ensemble learning on the best performing algorithms, we increased the accuracy of our approach to 91.86 %. Overall, our results demonstrate the strong potential of using audio visualization with deep learning algorithms to detect North Atlantic right whales. Further work in this field, such as exploring more algorithms, could further enhance the effectiveness of our approach.

关键词： Wavelet transforms Deep learning Accuracy Planets Whales Data visualization Ensemble learning detection algorithms Carbon sequestration Monitoring

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Research on road damage detection based on improved YOLOv8 in road scene

Research on road damage detection based on improved YOLOv8 i...

引用

Chinese Control and Decision Conference, CCDC

作者： Chen Zhou Zhongjun Xiao School of Electrical Engineering and Automation Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Road damage detection plays an important role in driving safety and road condition repair. Aiming at the existing inefficient and complex algorithms and methods, this paper provides an improved YOLOv8 detection algorithm: YOLOV8-MS(MPCA and Slim-neck), which combines precision and lightweight. Firstly, the Slim-neck structure is used in the head structure to reduce the computation. Secondly, the Multipath Coordinate Attention (MPCA) mechanism is added in the backbone structure, which increases the receptive field, improves the feature extraction ability of road defects, and reduces the interference of irrelevant features. Finally, we compare the improved YOLOv8 algorithm with other existing algorithms to verify the effectiveness of our proposed improvements. The experimental results show that in RDD2022 data set and self-built data set, compared with the original algorithm, the improved algorithm improves by 2.7% and 1.7% respectively with mAP50, and 1.9% and 0.6% respectively with MAP50-95. The computational complexity is reduced by 9.88%, which realizes the purpose of improving algorithm accuracy and lightweight.

关键词： Accuracy Roads Interference Maintenance engineering Feature extraction Safety detection algorithms

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GNSS Time Synchronization Attack detection Algorithm Based on Clock Bias Change Covariance

GNSS Time Synchronization Attack Detection Algorithm Based o...

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Peng Jia Qingmin Liao Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

This paper proposes a GNSS Time Synchronization Attack (TSA) detection algorithm based on clock bias change covariance. By leveraging the synchronization characteristics in the variation patterns of TSA signals, the algorithm effectively detects TSA by calculating the covariance of clock bias changes. Experimental results demonstrate that, compared to the clock drift correlation algorithm, this algorithm not only achieves higher detection sensitivity and accuracy, enabling a clear distinction between normal and TSA signals, but also operates faster, detecting TSA 17.86% earlier on average. Additionally, it reduces computational complexity, saving 17.86% of processing time. This practical detection algorithm is particularly suitable for resource-constrained hardware devices, enhancing the GNSS system's resistance to TSA, ensuring timing accuracy and system stability, and providing reliable security for critical infrastructure.

关键词： Global navigation satellite system Machine learning algorithms Accuracy Sensitivity Heuristic algorithms Hardware Critical infrastructure Synchronization detection algorithms Clocks

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Northeast cold vortex detection algorithm based on reanalysis data prediction

Northeast cold vortex detection algorithm based on reanalysi...

引用

Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Xi Jing Computer and Information Engineering college TianJin Normal University Tianjin China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

The Northeast Cold Vortex (NECV) is a significant atmospheric phenomenon characterized by a large-scale, high-altitude cold low-pressure system that predominantly influences the northeastern region of China and its adjacent areas. This persistent weather system typically maintains its activity for periods ranging from 3 to 4 days or longer, exhibiting substantial meteorological impacts. Current methodologies for determining the spatial extent of the NECV primarily rely on extensive data analysis, which involves substantial computational resources and prolonged data processing time. Based on the above reasons, this paper proposes a method to detect the northeast cold vortex by forecasting and reanalyzing data and combining it with satellite cloud images, which solves the problem of poor timeliness of detecting the northeast cold vortex by using ERA5, and improves the detection capability of the northeast cold vortex. Compared with the single use of satellite cloud image to detect the northeast cold vortex, its effect is significantly improved, and it has guiding significance for the detection methods of the northeast cold vortex.

关键词： Satellites Data analysis Clouds Prediction algorithms Distance measurement detection algorithms Information technology Forecasting Optimization Meteorology

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Dangerous Behavior detection Based on Convolutional Neural Network Algorithm

Dangerous Behavior Detection Based on Convolutional Neural N...

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Signal Processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Hua Zhang College of Information Science and Technology Bohai University Jinzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Behavior detection is an important research direction in the field of deep learning. CNN has achieved great success in the field of image classification, which promotes the research and development in the field of behavior detection. This paper studies dangerous behavior detection based on CNN algorithm. First of all, the problems of traditional risky behavior detection algorithms are studied, as well as the research progress and development trend of risky behavior detection based on CNN. Then, we study the basis of CNN algorithm, construct CNN network model, and study the working process of convolutional layer, pooling layer and fully connected layer. Then, we study CNN algorithm optimization, including Softmax regression optimization and back propagation algorithm optimization. Finally, KTH data set was selected for simulation experiment, and parameters such as learning rate, convolution kernel and Batch Size were scheduled. On this basis, comprehensive test of risky behavior was carried out. The results show that the CNN algorithm optimized in this paper can improve the accuracy of behavior recognition, which is of great significance in laboratory safety and other fields.

关键词： Training Solid modeling Accuracy Video sequences Signal processing algorithms Safety management Feature extraction Convolutional neural networks detection algorithms Optimization

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A Novel Extracellular Spike detection Algorithm Based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

A Novel Extracellular Spike Detection Algorithm Based on Imp...

引用

IEEE International Conference on Information Communication and Signal Processing (ICICSP)

作者： Canhong Long Zuozhi Liu School of Mathematics & Statistics Guizhou University of Finance and Economics Guiyang China School of Mathematics Northwest University Xi'an China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Precisely discerning spike signals amidst noise presents a significant and intricate challenge. To address this issue, we propose a new detection algorithm for extracellular spikes based on improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), aiming to achieve high accuracy and robust spike detection. Firstly, the extracellular recording signal is decomposed using CEEMDAN to obtain multiple intrinsic mode functions (IMFs). Subsequently, by analyzing the sample entropy of these IMFs, the important information hidden in the recorded signal can be prevented. Finally, the high-entropy noise components are discarded using wavelet soft thresholding. Experimental results from both simulated and real data demonstrate that the proposed method significantly outperforms existing methods by a large margin.

关键词： Empirical mode decomposition Accuracy Noise Entropy Robustness Recording Complexity theory Extracellular detection algorithms

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Block-Diagonal Belief-Propagation detection for

Block-Diagonal Belief-Propagation Detection for

引用

Information Communication Technologies Conference (ICTC)

作者： Bohan Yang Wenyue Zhou Zeqiong Tan Xiaosi Tan Yongming Huang Chuan Zhang Lab of Efficient Architectures for Digital-communication and Signal-processing (LEADS) National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350373523

ISBN: (纸本)9798350373530

Cell-Free Massive MIMO (CF-mMIMO) is a promising technology owing to its high user spectrum efficiency. However, CF-mMIMO faces the challenges of baseband signal processing, especially for uplink data detection. In this paper, Gaussian interference of approximation (GAI) Belief Propagation (BP) detection is utilized for CF-mMIMO systems to achieve near-optimal performance. Based on the natural feature of the block-diagonal (BD) channel matrix, a reduced-complexity GAI-BP, which is named BD-GAI-BP, is proposed to save the computational complexity while maintaining the performance merits. Numerical results show that compared with the state-of-the-art data detection algorithm, the GAI-BP detection supports 28.93% more UEs with $BER =10^{-3}$. The proposed BD-GAI-BP saves 10.94% computational costs while achieving comparable performance with the GAI-BP.

关键词： Degradation Massive MIMO Interference Signal processing Communications technology Computational efficiency detection algorithms

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Intelligent Malware detection: Enhancing Accuracy with Recurrent Neural Network

Intelligent Malware Detection: Enhancing Accuracy with Recur...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： C. A. Kandasamy P. Chandru J.K. Kanimozhi E. Loganathan N. Prakash A. Yoganathan Department of Computer Applications K.S.R. College of Engineering Tiruchengode

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Aim: The purpose of this research is to use Artificial Neural Networks (ANN) to create an intelligent malware detection system and evaluate how well it performs in comparison to ClamA V, a conventional antivirus program that relies on signatures. By using Recurrent Neural Networks (RNN) for feature extraction and classification, the study aims to increase detection accuracy. Materials and Methods: Two groups are taken into consideration in this study. Group 1 is the ANN-based intelligent malware detection system, ClamA V which uses with 26 samples. Group 2 is the 26- sample RNN signature-based detection algorithm. With a 95% confidence interval and a threshold of 0.05%, the G Power value is 80%. Both static and dynamic features taken from malware samples are processed by the suggested RNN -based malware detection method. The collection includes labelled To increase convergence, a backpropagation technique with an adaptable learning rate is used to train the ANN model. Results: The ANN- based intelligent malware detection system outperforms the ClamA V system in terms of detection accuracy (0.92 vs. 0.85), precision (0.88 vs. 0.80), recall (0.91 vs. 0.63), and F1-score (0.91 vs. 0.81). While the ClamA V detection accuracy ranges from 55.23% to 78.650/0, the RNN-based system's detection accuracy ranges from 85.42% to 98.76%. At a significance level of 0.0047 and a learning rate of 0.001, the best classification accuracy is obtained. Conclusion: This study shows that the suggested ANN-based malware detection system more especially, the RNN performs noticeably better than the conventional ClamA V system.

关键词： Recurrent neural networks Accuracy Artificial neural networks Feature extraction Market research Malware Distance measurement Pattern recognition Informatics detection algorithms

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