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检索条件"主题词=detection algorithms"

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DGL-YOLOv8 a Lightweight Yarn detection Algorithm

DGL-YOLOv8 a Lightweight Yarn Detection Algorithm

引用

Chinese Automation Congress (CAC)

作者： Shangjie Li Ren Jia Jiachao Dai School of Information Science & Engineering Zhejiang Sci-Tech University Hangzhou China Zhejiang Sci-Tech University Hangzhou China Changshan Research Institute Co. Ltd. Quzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Yarn, being a fundamental raw material in fabric manufacturing, directly influences the quality of the final product. To address potential challenges that may occur at various stages of yarn production, in this paper, we propose a lightweight and efficient recognition algorithm based on YOLOv8n, named DGL-YOLOv8. First, we enhanced the lightweight convolution DWConv by incorporating the MLCA attention mechanism to strengthen the connection between channel and spatial information. Then, we introduced the GhostModule lightweight module to refine the C2f structure, replacing the standard convolution layers with dynamic convolution layers. This allows the model to better capture features of objects at different scales. Finally, in the detection head, we adopted the concept of shared parameters, replacing the bulky three Detect modules of the original model with a single shared convolution, further reducing the model's parameters and computational cost. According to the experimental data, DGL-YOLOv8 achieved an mAP0.5 value of 90.1%, an improvement of 0.5% over YOLOv8n. The parameter count is 0.95M, and the computational load is 3.3 GFLOPs, representing reductions of 68% and 59%, respectively, compared to the base model. The results demonstrate that the improved model greatly increases the effectiveness of real-time object detection in complex environments, providing an effective solution for yarn production monitoring.

关键词： Adaptation models Attention mechanisms Convolution Computational modeling Production Feature extraction Computational efficiency Yarn detection algorithms Load modeling

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Research on Fire Smoke detection Algorithm Based on YOLOv8

Research on Fire Smoke Detection Algorithm Based on YOLOv8

引用

International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Xianglong Han Qingwei Shen Jin Huang Yuan Zhang school of Electronic and Information Engineering Anhui Jianzhu University Hefei China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

The SDE-YOLOv8 fire detection model is proposed to solve the problems of low accuracy and slow speed in the detection of fire *** on YOLOv8, SimAM attention mechanism is introduced in this model, and the C2f layer in the network structure is improved to obtain D-C2f module, which enhances the feature expression ability of the model in complex scenes and inhibits the interference of irrelevant ***, in order to improve the speed of model detection, a lightweight backbone network EfficientViT was used for feature extraction, which reduced the computing overhead during detection. The experimental results show that compared with the original YOLOv8 on DFS data set, SDE-YOLOv8's average accuracy mAP50 is increased by 2.2%, and the detection speed is increased by 14.3%, achieving a good improvement in accuracy and speed.

关键词： Accuracy Attention mechanisms Image color analysis Computational modeling Fires Interference Feature extraction Real-time systems detection algorithms

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Regularization and dimensionality reduction techniques for pattern detection in GitHub repositories.

Regularization and dimensionality reduction techniques for p...

引用

IEEE Colombian Communications Conference (COLCOM)

作者： Wilson Chango Geovanny Silva Pamela Buñay-Guisñan Santiago Logroño Computer Science and Electronics Information Technology ESPOCH Riobamba Ecuador Universidad Nacional de Chimborazo UNACH Riobamba Ecuador

ISBN: (数字)9798331504724

ISBN: (纸本)9798331504731

In the constant pursuit of improving the efficiency of machine learning models, various strategies have been employed to tackle the challenges related to the complexity and simplicity of datasets. Identifying the features that impact machine learning models or algorithms is essential. Avoiding overfitting is crucial, as it can result in irrelevant variables, increased computational cost, and generation of missing values that introduce significant biases, affecting the predictive capacity. A thorough evaluation of various machine learning algorithms was conducted to build an efficient predictive model, focusing on error reduction. algorithms such as PCA, IPCA, and KPCA were compared, along with regularization methods such as Linear, Lasso, Ridge, and Elastic Net to improve model accuracy. The analysis was carried out using a large dataset that encompasses all repositories on GitHub, providing a comprehensive representation of the information needed for the comparison and evaluation of the aforementioned algorithms. The results obtained offer crucial insights for the continuous optimization of the model, with an exclusive focus on the context of GitHub. The metrics used to analyze software projects in repositories such as GitHub provide an overview of the state and dynamics of a project in terms of activity, participation, and code quality. These metrics are fundamental for making informed decisions about software management and development. Identifying the features that impact machine learning models and evaluating various algorithms and regularization methods allow for the construction of efficient predictive models that can be applied in various areas of the software industry.

关键词： Software development management Machine learning algorithms Predictive models Prediction algorithms Software algorithms Measurement Heuristic algorithms Computational modeling Principal component analysis detection algorithms Pattern recognition Data models Codes Access control Continuous integration

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A Novel detection Scheme for Generalized Spatial Modulation System Based on Sparse Reconstruction

A Novel Detection Scheme for Generalized Spatial Modulation ...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Xiteng Liu Yang Zhang Chunsheng Qin Aviation Maintenance NCO Academy of Air Force Engineering University Xinyang China 94778 unit of Chinese People's Liberation Army Shanghai China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

Aiming at the problem of high complexity of traditional detection algorithms for generalized spatial modulation *** theory of compressed sensing was used to construct a signal detection model. According to the sparse property of generalized spatial modulation signals, a stagewise orthogonal matching pursuit detection algorithm based on sparse reconstruction was proposed. The active antenna index sequence was selected according to the correlation threshold judgment and the foreseen sparsity was not needed in the iterative process of the algorithm, the index set and signal residuals were *** the sent data was restored based on optimized termination condition and signal quantization. Analysis and experiments show the algorithm has smaller root mean square error and the detection performance is improved compared with reconstruction algorithms of the same category,and has substantially lower complexity compared with the maximum likelihood algorithm.

关键词： Sparse approximation Matching pursuit algorithms Modulation Reconstruction algorithms Approximation algorithms Prediction algorithms Iterative algorithms Complexity theory Indexes detection algorithms

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An FTN Sequence detection Algorithm Based on Soft Information Iteration and ADMM Optimization

An FTN Sequence Detection Algorithm Based on Soft Informatio...

引用

Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE International Conference on

作者： Zhiwen Li Qiang Chen Hui Xie School of Electronic and Information Engineering Chongqing Three Gorges University Wanzhou Chongqing China

ISBN: (数字)9798350378542

ISBN: (纸本)9798350378559

To address the severe inter-symbol interference (ISI) problem in faster-than-Nyquist (FTN) communication systems, this paper proposes an Iterative Soft ADMM detection (ISAD) sequence detection algorithm. The ISAD algorithm ingeniously integrates soft information iterative updating with ADMM optimization by alternately estimating residual ISI and updating symbol soft values while utilizing constellation prior knowledge and observation information to guide symbol estimation, ultimately achieving efficient detection in FTN systems. Compared with traditional methods, the ISAD algorithm effectively suppresses ISI while significantly reducing computational complexity, particularly in medium to high-order modulation schemes, where it exhibits outstanding performance. Simulation results demonstrate that the proposed algorithm can effectively suppress ISI in medium to high-order modulation and achieve bit error rate performance close to that of Nyquist orthogonal transmission. The proposed ADMM soft detection algorithm provides an effective solution for FTN communication systems.

关键词： Communication systems Simulation Bit error rate Modulation Symbols Interference Iterative algorithms detection algorithms Optimization Intelligent control

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Lightweight Road Vehicle detection Algorithm Based on Improved YOLOv8

Lightweight Road Vehicle Detection Algorithm Based on Improv...

引用

Computer Information Science and Application Technology (CISAT), International Conference on

作者： Xin Zhang

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

This paper addresses the problems of large model size, many parameters, high equipment requirements, and low recall rate in traditional vehicle detection models, and proposes a lightweight road vehicle detection algorithm based on the improved YOLOv8 (You Only Look Once). The improved detection algorithm reduces the number of parameters and the size of the model, which can effectively improve the detection speed and shorten the training time. This paper proposes a lightweight Shared Parameter detection head (SPDH), which can greatly reduce the number of model parameters while ensuring the detection accuracy. in the meantime SimAM attention mechanism, a lightweight and parameterless attention mechanism of convolutional neural networks, is added to the network to enhance the detection performance. Finally, the Star Block in StarNet proposed by Microsoft in 2024 is used to replace the Bottleneck in C2f for further lightening and guarantee performance. Experiments on BIT-Vehicle dataset demonstrate that the superior performance of the algorithm compared to the original model. Under the premise that The parameter count has been decreased by approximately 35% and the model’s size has been reduced by around 34%, resulting in a 1.2% increase in precision and a 1.8% increase in recall, and other evaluation indicators are roughly consistent with the benchmark model. The excellence of the improved algorithm is proved.

关键词： Training YOLO Information science Attention mechanisms Vehicle detection Road vehicles Stars Approximation algorithms Convolutional neural networks detection algorithms

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Belt Abnormal High-Temperature detection Algorithm Based on Improved YOLOv7-Tiny

Belt Abnormal High-Temperature Detection Algorithm Based on ...

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Lu Xiao Guolei Chen Yingjie Man Wendong Xiao Liang Yuan Qing Tao China University of Mining and Technology Xuzhou China Xinjiang Zhundong TBEA Energy Co. Ltd. Changji China Xinjiang Key Laboratory of Intelligent Production and Control of Open Pit Mines Changji China School of Mechanical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

This paper endeavors to enhance the current coal conveyor belt anomalous high-temperature detection algorithms by addressing their limitations in terms of precision and efficiency. We introduce a novel algorithm grounded in the optimized YOLOv7-tiny model, which leverages the Normalized Wasserstein Distance (NWD) loss function to enhance the representation of small targets. Furthermore, we incorporate the DyHead detection head to enrich feature representation and elevate detection capabilities. Our experimental findings validate that our enhanced algorithm surpasses the original YOLOv7-tiny algorithm, exhibiting notable gains in both detection accuracy and speed, especially when detecting small and visually similar targets.

关键词： Accuracy Automation Fuses Coal Belts Feature extraction detection algorithms Information technology

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Silent Face Anti-Spoofing detection Algorithm Based on Meta-learning and Attention Mechanism

Silent Face Anti-Spoofing Detection Algorithm Based on Meta-...

引用

Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE International Conference on

作者： Lan Li Hongzhe Dou Zijia Yu Xujuan Zhang Lijuan Sun Jundi Wang School of Computer Science & Artificial Intelligence Lanzhou Institute of Technology Lanzhou China

ISBN: (纸本)9798350355260

In the field of facial identity authentication, silent face anti-spoofing detection, as a key technology, to prevent fake photos and videos, is receiving widespread attention. To address the issue of low detection accuracy in existing algorithms that similar tasks have not appear in training, silent face anti-spoofing detection algorithm based on meta-learning and attention mechanism is proposed. First, by leveraging meta-learning to quickly adapt to shot samples, the proposed algorithm uses MAML as the main network to capture the commonalities between training tasks, allowing it to quickly adjust parameters in new scenarios or new attack types to achieve efficient detection. Then, in order to focus the model on extracting and analyzing the key features of facial regions, an attention mechanism is introduced in MAML to reduce the interference from the facial background region. Finally, a Fourier spectrum aided supervision branch is designed to improve the accuracy of classification by using the difference of the Fourier spectrum of real/fake face in the frequency domain. Experimental results show that the proposed algorithm achieves good accuracy on public NUAA dataset, with AUC and F1 scores of 0.99 and 0.90, respectively.

关键词： Metalearning Training Analytical models Adaptation models Accuracy Attention mechanisms Interference Feature extraction detection algorithms Faces

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An Efficient Cluster-Based Asynchronous Activity detection Algorithm for Cell-Free Massive MIMO Systems

An Efficient Cluster-Based Asynchronous Activity Detection A...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Chenhao You Ziheng Li Tong Wang Lin Gao Yufei Jiang School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, we propose an efficient cluster-based activity detection algorithm for asynchronous grant-free random access (GFRA) cell-free massive multiple-input multiple-output (CF-mMIMO) systems. To address the uncertain delay challenge in the asynchronous transmission scenario, we aim to enhance the accuracy of active user devices detection and meanwhile reducing the computational complexity of the detection process by utilizing an efficient cluster-based block coordinate descent (BCD) activity detection algorithm. This challenge is heightened in the context of ultra-reliable low-latency communications (URLLC), where precise and timely detection of active users is crucial. To enhance the efficiency of the detection process, we investigate an active set strategy to reduce the number of potential active user devices requiring identification. The accuracy of active user device detection for asynchronous GFRA in CF-mMIMO systems is significantly enhanced by the implementation of the proposed cluster-based BCD algorithm with active set. Simulation results demonstrate that the proposed algorithm achieves superior detection performance with lower computational complexity compared to the traditional algorithms.

关键词： Wireless communication Performance evaluation Accuracy Simulation Signal processing algorithms Clustering algorithms Ultra reliable low latency communication Signal processing detection algorithms Computational complexity

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Massive MIMO Belief Propagation detection Using DIP with DNN-Trained Scaling Factor

Massive MIMO Belief Propagation Detection Using DIP with DNN...

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IEEE Conference on Wireless Communications and Networking

作者： Junta Tachibana Mondher Bouazizi Tomoaki Ohtsuki Graduate School of Science and Technology Keio University Department of Information and Computer Science Faculty of Science and Technology Keio University Yokohama Japan

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Belief propagation (BP) detection is a technique for separating and detecting incoming signals with minimal complexity in massive multiple-input multiple-output (MIMO) systems. However, because of the interference and noise that remain in the received signals even after attempting to remove them through conventional techniques, errors manifest in the transmitted messages. Due to the MIMO channel's numerous loops, a message containing mistakes spreads across the factor graph leading to a degradation in the BP's convergence properties and detection performance. In this paper, we propose a BP detection using deep image prior (DIP) with deep neural network (DNN)-trained scaling factor. By applying DIP to the BP detection algorithm, we achieve a reduction in residual interference and noise. Post DIP application, there is a modification in the variance of both interference and noise components. To align it more accurately with its true value and enhance message reliability, we adjust the variance using scaling factors trained through DNN-based damped BP (DNN-dBP). Using computer simulations, we demonstrate that applying DIP helps decrease the power of the residual interference and noise after removing the interference at each iteration in the BP detection. It is also shown that the proposed method improves the detection performance compared to the normal BP detection, BP detection without DIP when training the scaling factors of the variance, and BP detection to which DIP is applied without training the scaling factors of the variance.

关键词： Training Degradation Noise Interference Massive MIMO Reliability detection algorithms

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