Digital Signal Processing最新文献_第3页

Interpretable ADMM-CSNet for interrupted sampling repeater jamming suppression 用于抑制中断采样中继器干扰的可解释 ADMM-CSNet

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-31 DOI: 10.1016/j.dsp.2024.104850

Quan Huang , Shaopeng Wei , Lei Zhang

Interrupted sampling repeater jamming (ISRJ) is a category of coherent jamming that greatly influences radars' detection performance. Since the ISRJ has greater power than true targets, ISRJ signals can be removed in the time domain. Due to frequency band loss, grating lobes will be produced if pulse compression (PC) is performed directly, which may generate false targets. Compressive sensing (CS) is an effective method to restore the original PC signal. However, it is challenging for classic CS approaches to manually select the optimization parameters (e.g., penalty parameters, step sizes, etc.) in different ISRJ backgrounds. In this article, a network method based on the Alternating Direction Method of Multipliers (ADMM), named ADMM-CSNet, is introduced to solve the problem. Based on the strong learning capacity of the deep network, all parameters in the ADMM are learned from radar data utilizing back-propagation rather than manually selecting in traditional CS techniques. Compared with classic CS approaches, a higher ISRJ removal signal restoration accuracy is reached faster. Simulation experiments indicate the proposal performs effectively and accurately for ISRJ removal signal reconstruction.

中断采样中继器干扰（ISRJ）是相干干扰的一种，对雷达的探测性能有很大影响。由于 ISRJ 比真实目标的功率更大，因此 ISRJ 信号可以在时域中去除。由于频带损耗，如果直接进行脉冲压缩（PC），就会产生光栅裂片，从而产生假目标。压缩传感（CS）是恢复原始 PC 信号的有效方法。然而，在不同的 ISRJ 背景下，手动选择优化参数（如惩罚参数、步长等）对经典 CS 方法来说是一项挑战。本文引入了一种基于交替方向乘法（ADMM）的网络方法来解决这一问题，命名为 ADMM-CSNet。基于深度网络的强大学习能力，ADMM 中的所有参数都是利用反向传播从雷达数据中学习的，而不是传统 CS 技术中的手动选择。与传统的 CS 方法相比，该方法能更快地达到更高的 ISRJ 消除信号恢复精度。仿真实验表明，该方案能有效、准确地重建 ISRJ 消除信号。

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引用次数: 0

Self-learning based joint multi image super-resolution and sub-pixel registration 基于自学习的多图像联合超分辨率和子像素配准

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-31 DOI: 10.1016/j.dsp.2024.104837

Hansol Kim , Sukho Lee , Moon Gi Kang

Multi Image Super-resolution (MISR) refers to the task of enhancing the spatial resolution of a stack of low-resolution (LR) images representing the same scene. Although many deep learning-based single image super-resolution (SISR) technologies have recently been developed, deep learning has not been widely exploited for MISR, even though it can achieve higher reconstruction accuracy because more information can be extracted from the stack of LR images. One of the primary obstacles encountered by deep networks when addressing the MISR problem is the variability in the number of LR images that act as input to the network. This impedes the feasibility of adopting an end-to-end learning approach, because the varying number of input images makes it difficult to construct a training dataset for the network. Another challenge arises from the requirement to align the LR input images to generate high-resolution (HR) image of high quality, which requires complex and sophisticated methods.

In this paper, we propose a self-learning based method that can simultaneously perform super-resolution and sub-pixel registration of multiple LR images. The proposed method trains a neural network with only the LR images as input and without any true target HR images; i.e., the proposed method requires no extra training dataset. Therefore, it is easy to use the proposed method to deal with different numbers of input images. To our knowledge this is the first time that a neural network is trained using only LR images to perform a joint MISR and sub-pixel registration. Experimental results confirmed that the HR images generated by the proposed method achieved better results in both quantitative and qualitative evaluations than those generated by other deep learning-based methods.

多图像超分辨率（MISR）是指增强代表同一场景的低分辨率（LR）图像堆栈的空间分辨率。尽管最近开发出了许多基于深度学习的单图像超分辨率（SISR）技术，但深度学习尚未被广泛用于 MISR，尽管它可以实现更高的重建精度，因为可以从一叠低分辨率图像中提取更多信息。深度网络在解决 MISR 问题时遇到的主要障碍之一是作为网络输入的 LR 图像数量的不稳定性。这阻碍了采用端到端学习方法的可行性，因为输入图像数量的变化使得网络难以构建训练数据集。本文提出了一种基于自学习的方法，可同时对多幅 LR 图像进行超分辨率和子像素配准。本文提出的方法只将 LR 图像作为输入，而不使用任何真实的目标 HR 图像来训练神经网络；也就是说，本文提出的方法不需要额外的训练数据集。因此，建议的方法很容易处理不同数量的输入图像。据我们所知，这是第一次仅使用 LR 图像来训练神经网络，以执行 MISR 和子像素联合配准。实验结果证实，与其他基于深度学习的方法相比，拟议方法生成的 HR 图像在定量和定性评估方面都取得了更好的结果。

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引用次数: 0

Dynamic mode decomposition-based technique for cross-term suppression in the Wigner-Ville distribution 基于动态模式分解的维格纳-维尔分布交叉项抑制技术

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-29 DOI: 10.1016/j.dsp.2024.104833

Alavala Siva Sankar Reddy, Ram Bilas Pachori

This paper presents a new method for time-frequency representation (TFR) using dynamic mode decomposition (DMD) and Wigner-Ville distribution (WVD), which is termed as DMD-WVD. The proposed method helps in removing cross-term in WVD-based TFR. In the suggested method, the DMD decomposes the multi-component signal into a set of modes where each mode is considered as mono-component signal. The analytic modes of these obtained mono-component signals are computed using the Hilbert transform. The WVD is computed for each analytic mode and added together to obtain cross-term free TFR based on the WVD. The effectiveness of the proposed method for TFR is evaluated using Rényi entropy (RE). Experimental results for synthetic signals namely, multi-component amplitude modulated signal, multi-component linear frequency modulated (LFM) signal, multi-component nonlinear frequency modulated (NLFM) signal, multi-component signal consisting of LFM and NLFM mono-component signal, multi-component signal consisting of sinusoidal and quadratic frequency modulated mono-component signals, and synthetic mechanical bearing fault signal and natural signals namely, electroencephalogram (EEG) and bat echolocation signals are presented in order to show the effectiveness of the proposed method for TFR. It is clear from the results that the proposed method suppresses cross-term effectively as compared to the other existing methods namely, smoothed pseudo WVD (SPWVD), empirical mode decomposition (EMD)-WVD, EMD-SPWVD, variational mode decomposition (VMD)-WVD, VMD-SPWVD, and DMD-SPWVD.

本文提出了一种使用动态模式分解（DMD）和维格纳-维尔分布（WVD）进行时频表示（TFR）的新方法，称为 DMD-WVD。建议的方法有助于消除基于 WVD 的 TFR 中的交叉项。在建议的方法中，DMD 将多分量信号分解为一组模式，其中每个模式都被视为单分量信号。利用希尔伯特变换计算这些单分量信号的解析模式。计算每个解析模式的 WVD 值，并将其相加，以获得基于 WVD 值的无跨期 TFR。利用雷尼熵 (RE) 评估了所提出的 TFR 方法的有效性。实验结果包括合成信号（即多分量幅度调制信号、多分量线性频率调制（LFM）信号、多分量非线性频率调制（NLFM）信号、由 LFM 和 NLFM 单分量信号组成的多分量信号、由正弦和二次频率调制单分量信号组成的多分量信号）、合成机械轴承故障信号以及自然信号（即脑电图（EEG）和蝙蝠回声定位信号），以显示所提方法对 TFR 的有效性。结果表明，与其他现有方法（即平滑伪 WVD（SPWVD）、经验模式分解（EMD）-WVD、EMD-SPWVD、变异模式分解（VMD）-WVD、VMD-SPWVD 和 DMD-SPWVD）相比，拟议方法能有效抑制交叉项。

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引用次数: 0

DuINet: A dual-branch network with information exchange and perceptual loss for enhanced image denoising DuINet：用于增强图像去噪的信息交换和感知损失双分支网络

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104835

Xiaotong Wang , Yibin Tang , Cheng Yao , Yuan Gao , Ying Chen

Image denoising is a fundamental task in image processing and low-level computer vision, often necessitating a delicate balance between noise removal and the preservation of fine details. In recent years, deep learning approaches, particularly those utilizing various neural network architectures, have shown significant promise in addressing this challenge. In this study, we propose DuINet, a novel dual-branch network specifically designed to capture complementary aspects of image information. DuINet integrates an information exchange module that facilitates effective feature sharing between the branches, and it incorporates a perceptual loss function aimed at enhancing the visual quality of the denoised images. Extensive experimental results demonstrate that DuINet surpasses existing dual-branch models and several state-of-the-art convolutional neural network (CNN)-based methods, particularly under conditions of severe noise where preserving fine details and textures is critical. Moreover, DuINet maintains competitive performance in terms of the LPIPS index when compared to deeper or larger networks such as Restormer and MIRNet, underscoring its ability to deliver high visual quality in denoised images.

图像去噪是图像处理和底层计算机视觉中的一项基本任务，通常需要在去除噪声和保留精细细节之间取得微妙的平衡。近年来，深度学习方法，特别是那些利用各种神经网络架构的方法，在应对这一挑战方面显示出了巨大的潜力。在本研究中，我们提出了一种新颖的双分支网络 DuINet，专门用于捕捉图像信息的互补方面。DuINet 集成了一个信息交换模块，可促进各分支之间有效的特征共享，同时还集成了一个感知损失函数，旨在提高去噪图像的视觉质量。广泛的实验结果表明，DuINet 超越了现有的双分支模型和几种最先进的基于卷积神经网络（CNN）的方法，尤其是在保留精细细节和纹理至关重要的严重噪声条件下。此外，与 Restormer 和 MIRNet 等更深层次或更大型的网络相比，DuINet 在 LPIPS 指数方面保持了极具竞争力的性能，突出表明它有能力为去噪图像提供高质量的视觉效果。

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引用次数: 0

Full-duplex cooperative relaying systems for simultaneous wireless information and power transfer with non-orthogonal multiple access 利用非正交多址同时进行无线信息和功率传输的全双工合作中继系统

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104817

Huu Q. Tran , Lam Hoang Kham , Ho Van Khuong

In this research, we propose a system model based on cooperative non-orthogonal multiple access (NOMA) for simultaneous wireless information and power transfer (SWIPT) within a full-duplex (FD) communication framework. We investigate two protocols - time switching protocol (TSR) and power splitting protocol (PSR) - designed to accommodate delay-tolerant-transmission (DTT) as well as delay-limited-transmission (DLT), thereby improving data processing and energy harvesting (EH). We present explicit formulas for pivotal performance measures such as energy efficiency, ergodic rate, throughput, and outage probability. These performance measures are thoroughly evaluated in numerous specifications, encompassing inter-user separation, required spectral efficiency, EH efficiency, time and power splitting ratios in moderate-to-high signal-to-noise ratio scenarios. The results expose improved EH efficiency, hence meliorated transmission reliability. Importantly, NOMA in the proposed system model is proved to be considerably better than traditional orthogonal multiple access.

在这项研究中，我们提出了一种基于合作式非正交多址接入（NOMA）的系统模型，用于全双工（FD）通信框架内的同步无线信息和功率传输（SWIPT）。我们研究了两种协议--时间切换协议（TSR）和功率分配协议（PSR）--旨在适应延迟容忍传输（DTT）和延迟限制传输（DLT），从而改进数据处理和能量收集（EH）。我们提出了关键性能指标的明确公式，如能效、遍历率、吞吐量和中断概率。这些性能指标在多种规格中进行了全面评估，包括用户间分离、所需频谱效率、EH 效率、中高信噪比情况下的时间和功率分配比例。结果表明，EH 效率得到了提高，从而改善了传输可靠性。重要的是，事实证明，拟议系统模型中的 NOMA 比传统的正交多址接入要好得多。

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引用次数: 0

Multimodal evaluation of customer satisfaction from voicemails using speech and language representations 利用语音和语言表征对语音邮件中的客户满意度进行多模态评估

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104820

Luis Felipe Parra-Gallego , Tomás Arias-Vergara , Juan Rafael Orozco-Arroyave

Customer satisfaction (CS) evaluation in call centers is essential for assessing service quality but commonly relies on human evaluations. Automatic evaluation systems can be used to perform CS analyses, enabling the evaluation of larger datasets. This research paper focuses on CS analysis through a multimodal approach that employs speech and language representations derived from the real-world voicemails. Additionally, given the similarity between the evaluation of a provided service (which may elicit different emotions in customers) and the automatic classification of emotions in speech, we also explore the topic of emotion recognition with the well-known corpus IEMOCAP which comprises 4-classes corresponding to different emotional states. We incorporated a language representation with word embeddings based on a CNN-LSTM model, and three different self-supervised learning (SSL) speech encoders, namely Wav2Vec2.0, HuBERT, and WavLM. A bidirectional alignment network based on attention mechanisms is employed for synchronizing speech and language representations. Three different fusion strategies are also explored in the paper. According to our results, the GGF model outperformed both, unimodal and other multimodal methods in the 4-class emotion recognition task on the IEMOCAP dataset and the binary CS classification task on the KONECTADB dataset. The study also demonstrated superior performance of our methodology compared to previous works on KONECTADB in both unimodal and multimodal approaches.

呼叫中心的客户满意度（CS）评估对于评估服务质量至关重要，但通常依赖于人工评估。自动评估系统可用于执行 CS 分析，从而对更大的数据集进行评估。本研究论文侧重于通过多模态方法进行 CS 分析，该方法采用了从真实世界语音邮件中提取的语音和语言表征。此外，鉴于对所提供服务的评估（可能会引发客户的不同情绪）与语音中情绪的自动分类之间存在相似性，我们还利用著名的语料库 IEMOCAP 探索了情绪识别的主题，该语料库由对应于不同情绪状态的 4 个类别组成。我们采用了基于 CNN-LSTM 模型的单词嵌入语言表示法，以及三种不同的自监督学习（SSL）语音编码器，即 Wav2Vec2.0、HuBERT 和 WavLM。在同步语音和语言表征时，采用了基于注意力机制的双向对齐网络。文中还探讨了三种不同的融合策略。研究结果表明，在 IEMOCAP 数据集的四类情感识别任务和 KONECTADB 数据集的二元 CS 分类任务中，GGF 模型的表现优于单模态方法和其他多模态方法。这项研究还表明，与之前在 KONECTADB 上使用的单模态和多模态方法相比，我们的方法具有更优越的性能。

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引用次数: 0

An optimization synchrosqueezed fractional wavelet transform for TFF analysis and its applications 用于 TFF 分析的优化同步queezed 小数小波变换及其应用

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104819

Yong Guo , Lidong Yang

To enhance the resolution of synchrosqueezing transform (SST) in non-stationary signal representation, an optimization synchrosqueezed fractional wavelet transform (SSFRWT) is proposed, which possesses rigorous mathematical principle and high resolution. First, the definition, properties, and principles of SSFRWT are presented. On this basis, a time-fractional-frequency (TFF) analysis method is established utilizing SSFRWT. The experimental results demonstrate that SSFRWT is capable of establishing a high-resolution TFF representation for chirp-type signals, surpassing existing methods in terms of noise robustness and energy concentration. Lastly, leveraging the signal TFF representation, SSFRWT is successfully applied to the chirp signal parameter estimation and multi-component signal separation, yielding superior estimation results and reconstructed signal compared to SST. Notably, SSFRWT is also innovatively employed in the field of optical measurement, achieving high-precision measurement of the curvature radius of convex lens.

为了提高同步阙值变换（SST）在非平稳信号表示中的分辨率，提出了一种优化同步阙值分数小波变换（SSFRWT），它具有严谨的数学原理和高分辨率。首先，介绍了 SSFRWT 的定义、特性和原理。在此基础上，利用 SSFRWT 建立了时间-分数-频率（TFF）分析方法。实验结果表明，SSFRWT 能够为啁啾信号建立高分辨率的 TFF 表示，在噪声鲁棒性和能量集中方面超越了现有方法。最后，利用信号 TFF 表示，SSFRWT 成功应用于啁啾信号参数估计和多分量信号分离，与 SST 相比，获得了更优越的估计结果和重建信号。值得一提的是，SSFRWT 还创新性地应用于光学测量领域，实现了凸透镜曲率半径的高精度测量。

引用次数: 0

Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems 无小区多ARS通信系统信道估计阶段的空间先导重分配算法

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104830

Van Son Nguyen , Bui Anh Duc , Tran Manh Hoang , Xuan Nam Tran , Pham Thanh Hiep , Nguyen Thu Phuong

In this paper, we investigate the user throughputs of a Cell-Free (CF) system with multiple aerial relay stations (ARSs), where each ARS is defined as an unmanned aerial vehicle (UAV)-mounted relay station. The system operates under a decode-and-forward (DF) protocol and facilitates connectivity between a terrestrial base station (TBS) and terrestrial users. ARSs are equipped with multiple antennas and simultaneously serve users that are outfitted with single antennas and distributed in a specific area. Additionally, a small-cell (SC) system based on the CF structure, where each ARS serves one user with the best channel conditions, is also considered. We analyze system communication in two stages, including user-ARS links and ARS-TBS links, and then we derive expressions for the data rate of users and ARSs. Moreover, we propose the spatial pilot reassignment (SPR) algorithm to optimize pilot assignment, enhancing channel estimation over random pilot assignment methods. The user throughput is evaluated by altering several system parameters, including the with/without data power control, the number of users, the number of ARSs, and the time interval allocated for channel estimation. The results show that the SPR algorithm improves throughput by about 10% compared to the random pilot assignment method at a 90%-likely user throughput, which is equal to a cumulative distribution function value of 0.1.

本文研究了带有多个空中中继站（ARS）的无蜂窝（CF）系统的用户吞吐量，其中每个 ARS 被定义为无人机（UAV）安装的中继站。该系统根据解码转发（DF）协议运行，促进地面基站（TBS）与地面用户之间的连接。中继站配备多根天线，同时为配备单根天线并分布在特定区域的用户提供服务。此外，我们还考虑了基于 CF 结构的小蜂窝（SC）系统，即每个 ARS 在最佳信道条件下为一个用户提供服务。我们分两个阶段分析系统通信，包括用户-ARS 链路和 ARS-TBS 链路，然后推导出用户和 ARS 的数据速率表达式。此外，我们还提出了优化先导分配的空间先导重分配（SPR）算法，与随机先导分配方法相比，该算法增强了信道估计能力。通过改变几个系统参数，包括有/无数据功率控制、用户数量、ARS 数量以及分配给信道估计的时间间隔，对用户吞吐量进行了评估。结果表明，在 90% 的用户吞吐量（相当于累积分布函数值 0.1）时，SPR 算法比随机先导分配方法提高了约 10% 的吞吐量。

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引用次数: 0

A novel spatial pyramid-enhanced indoor visual positioning method 一种新颖的空间金字塔增强型室内视觉定位方法

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-28 DOI: 10.1016/j.dsp.2024.104831

Jiaqiang Yang , Danyang Qin , Huapeng Tang , Sili Tao , Haoze Bie , Lin Ma

As a key application of Internet of Things (IoT) technology, visual localization plays an important role in everyday life. However, pedestrians in images can obstruct environmental features, negatively impacting the performance of visual localization systems. To address this issue, we propose a Spatial Pyramid-Enhanced MixVPR visual localization method (SPE-VL) that aims to enhance image feature descriptions through multi-scale spatial information, thereby mitigating the effects of pedestrian occlusion on localization accuracy. The SPE-VL method is divided into two main phases: sensor-based matching range constraint and image feature extraction and matching. In the matching range constraint phase, we propose a direction decision method based on a machine learning classifier that utilizes smartphone sensor data to restrict the direction of image matching, reducing the likelihood of mismatches. In the feature extraction and matching phase, we propose a Transformer-based feature cross-enhancement method that leverages local features and spatial contextual information to enhance features, improving both image retrieval accuracy and localization precision. Experimental results indicate that the SPE-VL method demonstrates higher localization accuracy and robustness compared to existing state-of-the-art methods, providing new insights and solutions for the application of visual localization in complex environments.

作为物联网（IoT）技术的一项重要应用，视觉定位在日常生活中发挥着重要作用。然而，图像中的行人会遮挡环境特征，对视觉定位系统的性能产生负面影响。针对这一问题，我们提出了一种空间金字塔增强混合VPR可视化定位方法（SPE-VL），旨在通过多尺度空间信息增强图像特征描述，从而减轻行人遮挡对定位精度的影响。SPE-VL 方法分为两个主要阶段：基于传感器的匹配范围约束和图像特征提取与匹配。在匹配范围约束阶段，我们提出了一种基于机器学习分类器的方向判定方法，利用智能手机传感器数据来限制图像匹配的方向，从而降低不匹配的可能性。在特征提取和匹配阶段，我们提出了一种基于变换器的特征交叉增强方法，利用局部特征和空间上下文信息来增强特征，从而提高图像检索准确率和定位精度。实验结果表明，与现有的先进方法相比，SPE-VL 方法具有更高的定位精度和鲁棒性，为复杂环境中的视觉定位应用提供了新的见解和解决方案。

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引用次数: 0

An intelligent recognition method of factory personnel behavior based on deep learning 基于深度学习的工厂人员行为智能识别方法

IF 2.9 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing

Pub Date : 2024-10-24 DOI: 10.1016/j.dsp.2024.104834

Qilei Xu , Longen Liu , Fangkun Zhang , Xu Ma , Ke Sun , Fengying Cui

The real-time and accurate recognition of abnormal behavior among factory personnel helps enhance their awareness of hazardous environments, thereby reducing the occurrence of accidents. This paper proposes a behavior recognition network based on an attention mechanism and a high-efficiency convolution module. The Bi-Level Routing Attention was introduced to the backbone network, thus enhancing the attention of the recognition network to the target region effectively. The recognition accuracy was further strengthened by improving the neck network based on the ConvNeXt Block module while reducing the model complexity. Thirteen additional recognition models were constructed to enhance the original network from various perspectives. Subsequently, the mean average precision and detection speed of each model were evaluated. Experimental results demonstrated that the detection accuracy of the target recognition network proposed in this paper has been significantly improved, the detection speed meets the real-time requirements, and the comprehensive performance is the most superior compared with other diverse and improved networks. The proposed recognition model can accurately identify a variety of factory personnel's abnormal behaviors in real-time, and it has practical application significance for the problem of personnel safety identification in the factory.

实时、准确地识别工厂人员的异常行为有助于提高他们对危险环境的认识，从而减少事故的发生。本文提出了一种基于关注机制和高效卷积模块的行为识别网络。在骨干网络中引入了双层路由注意机制，从而有效增强了识别网络对目标区域的注意。通过改进基于 ConvNeXt Block 模块的颈部网络，进一步提高了识别准确率，同时降低了模型的复杂度。另外还构建了 13 个识别模型，从不同角度增强了原始网络。随后，对每个模型的平均精度和检测速度进行了评估。实验结果表明，本文提出的目标识别网络的检测精度有了显著提高，检测速度满足实时性要求，与其他不同的改进网络相比，综合性能最为优越。本文提出的识别模型可以实时准确地识别工厂人员的各种异常行为，对解决工厂人员安全识别问题具有实际应用意义。

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引用次数: 0