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Human Activity Recognition Using a CNN with an Enhanced Convolutional Block Attention Module 认领 引用
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作者 HU Biling TONG Yu 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2026年第1期10-24,共15页
WiFi-based human activity recognition(HAR)provides a non-intrusive approach for ubiquitous monitoring;however,achieving both high accuracy and robustness simultaneously remains a significant challenge.This paper propo... WiFi-based human activity recognition(HAR)provides a non-intrusive approach for ubiquitous monitoring;however,achieving both high accuracy and robustness simultaneously remains a significant challenge.This paper proposes a Convolutional Neural Network with Enhanced Convolutional Block Attention Module(CNN-ECBAM)framework.The approach systematically converts raw Channel State Information(CSI)into pseudo-color images,effectively preserving essential signal characteristics for deep neural network processing.The core innovation is an Enhanced Convolutional Block Attention Module(ECBAM),tailored to CSI data characteristics,which integrates Efficient Channel Attention(ECA)and Multi-Scale Spatial Attention(MSSA).By employing learnable adaptive fusion weights,it achieves dynamic synergy between channel and spatial features,enabling the network to capture highly discriminative spatiotemporal patterns.The ECBAM module is integrated into a unified Convolutional Neural Network(CNN)to form the overall CNN-ECBAM model.Experimental results on the UT-HAR and NTU-Fi_HAR datasets demonstrate that CNN-ECBAM achieves competitive performance in recognition accuracy and outperforms mainstream baseline models.Specifically,it attains 99.20%accuracy on UT-HAR(surpassing ResNet-18 at 98.60%)and achieves 100%accuracy on NTU-Fi_HAR(exceeding GAF-CNN at 99.62%).These results validate the effectiveness of the proposed method for high-precision and reliable WiFi-based HAR. 展开更多
关键词 human activity recognition deep learning channel state information Enhanced Convolutional Block Attention Module(ECBAM) pseudo-color images
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Enhanced Cutaneous Melanoma Segmentation in Dermoscopic Images Using a Dual U-Net Framework with Multi-Path Convolution Block Attention Module and SE-Res-Conv 认领 引用
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作者 Kun Lan Feiyang Gao +2 位作者 Xiaoliang Jiang Jianzhen Cheng Simon Fong 《Computers, Materials & Continua》 SCIE EI 2025年第9期4805-4824,共20页
With the continuous development of artificial intelligence and machine learning techniques,there have been effective methods supporting the work of dermatologist in the field of skin cancer detection.However,object si... With the continuous development of artificial intelligence and machine learning techniques,there have been effective methods supporting the work of dermatologist in the field of skin cancer detection.However,object significant challenges have been presented in accurately segmenting melanomas in dermoscopic images due to the objects that could interfere human observations,such as bubbles and scales.To address these challenges,we propose a dual U-Net network framework for skin melanoma segmentation.In our proposed architecture,we introduce several innovative components that aim to enhance the performance and capabilities of the traditional U-Net.First,we establish a novel framework that links two simplified U-Nets,enabling more comprehensive information exchange and feature integration throughout the network.Second,after cascading the second U-Net,we introduce a skip connection between the decoder and encoder networks,and incorporate a modified receptive field block(MRFB),which is designed to capture multi-scale spatial information.Third,to further enhance the feature representation capabilities,we add a multi-path convolution block attention module(MCBAM)to the first two layers of the first U-Net encoding,and integrate a new squeeze-and-excitation(SE)mechanism with residual connections in the second U-Net.To illustrate the performance of our proposed model,we conducted comprehensive experiments on widely recognized skin datasets.On the ISIC-2017 dataset,the IoU value of our proposed model increased from 0.6406 to 0.6819 and the Dice coefficient increased from 0.7625 to 0.8023.On the ISIC-2018 dataset,the IoU value of proposed model also improved from 0.7138 to 0.7709,while the Dice coefficient increased from 0.8285 to 0.8665.Furthermore,the generalization experiments conducted on the jaw cyst dataset from Quzhou People’s Hospital further verified the outstanding segmentation performance of the proposed model.These findings collectively affirm the potential of our approach as a valuable tool in supporting clinical decision-making in the field of skin cancer detection,as well as advancing research in medical image analysis. 展开更多
关键词 Dual U-Net skin lesion segmentation squeeze-and-excitation modified receptive field block multi-path convolution block attention module
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ANC: Attention Network for COVID-19 Explainable Diagnosis Based on Convolutional Block Attention Module 认领 引用 被引量：14
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作者 Yudong Zhang Xin Zhang Weiguo Zhu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第6期1037-1058,共22页
Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network forCOVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed t... Aim: To diagnose COVID-19 more efficiently and more correctly, this study proposed a novel attention network forCOVID-19 (ANC). Methods: Two datasets were used in this study. An 18-way data augmentation was proposed toavoid overfitting. Then, convolutional block attention module (CBAM) was integrated to our model, the structureof which is fine-tuned. Finally, Grad-CAM was used to provide an explainable diagnosis. Results: The accuracyof our ANC methods on two datasets are 96.32% ± 1.06%, and 96.00% ± 1.03%, respectively. Conclusions: Thisproposed ANC method is superior to 9 state-of-the-art approaches. 展开更多
关键词 Deep learning convolutional block attention module attention mechanism COVID-19 explainable diagnosis
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Traffic Sign Recognition for Autonomous Vehicle Using Optimized YOLOv7 and Convolutional Block Attention Module 认领 引用 被引量：2
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作者 P.Kuppusamy M.Sanjay +1 位作者 P.V.Deepashree C.Iwendi 《Computers, Materials & Continua》 SCIE EI 2023年第10期445-466,共22页
The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine ... The infrastructure and construction of roads are crucial for the economic and social development of a region,but traffic-related challenges like accidents and congestion persist.Artificial Intelligence(AI)and Machine Learning(ML)have been used in road infrastructure and construction,particularly with the Internet of Things(IoT)devices.Object detection in Computer Vision also plays a key role in improving road infrastructure and addressing trafficrelated problems.This study aims to use You Only Look Once version 7(YOLOv7),Convolutional Block Attention Module(CBAM),the most optimized object-detection algorithm,to detect and identify traffic signs,and analyze effective combinations of adaptive optimizers like Adaptive Moment estimation(Adam),Root Mean Squared Propagation(RMSprop)and Stochastic Gradient Descent(SGD)with the YOLOv7.Using a portion of German traffic signs for training,the study investigates the feasibility of adopting smaller datasets while maintaining high accuracy.The model proposed in this study not only improves traffic safety by detecting traffic signs but also has the potential to contribute to the rapid development of autonomous vehicle systems.The study results showed an impressive accuracy of 99.7%when using a batch size of 8 and the Adam optimizer.This high level of accuracy demonstrates the effectiveness of the proposed model for the image classification task of traffic sign recognition. 展开更多
关键词 Object detection traffic sign detection YOLOv7 convolutional block attention module road sign detection Adam
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MobileNet network optimization based on convolutional block attention module 认领 引用 被引量：3
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作者 ZHAO Shuxu MEN Shiyao YUAN Lin 《Journal of Measurement Science and Instrumentation》 CAS CSCD 2022年第2期225-234,共10页
Deep learning technology is widely used in computer vision.Generally,a large amount of data is used to train the model weights in deep learning,so as to obtain a model with higher accuracy.However,massive data and com... Deep learning technology is widely used in computer vision.Generally,a large amount of data is used to train the model weights in deep learning,so as to obtain a model with higher accuracy.However,massive data and complex model structures require more calculating resources.Since people generally can only carry and use mobile and portable devices in application scenarios,neural networks have limitations in terms of calculating resources,size and power consumption.Therefore,the efficient lightweight model MobileNet is used as the basic network in this study for optimization.First,the accuracy of the MobileNet model is improved by adding methods such as the convolutional block attention module(CBAM)and expansion convolution.Then,the MobileNet model is compressed by using pruning and weight quantization algorithms based on weight size.Afterwards,methods such as Python crawlers and data augmentation are employed to create a garbage classification data set.Based on the above model optimization strategy,the garbage classification mobile terminal application is deployed on mobile phones and raspberry pies,realizing completing the garbage classification task more conveniently. 展开更多
关键词 MobileNet convolutional block attention module(CBAM) model pruning and quantization edge machine learning
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基于MSMDC-CBAM-BiGRU的区域建筑能耗预测研究 认领 引用
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作者 江镇毅 屠宇 《建筑节能（中英文）》 CAS 2026年第7期35-44,共10页
为提升区域建筑能耗预测的精度与适应性,提出一种融合多尺度空洞卷积(MSMDC)、卷积块注意力机制(CBAM)与双向门控循环单元(BiGRU)的深度学习模型。该模型通过MSMDC提取多层次特征,结合CBAM动态分配特征权重,并利用BiGRU建模时间序列的... 为提升区域建筑能耗预测的精度与适应性,提出一种融合多尺度空洞卷积(MSMDC)、卷积块注意力机制(CBAM)与双向门控循环单元(BiGRU)的深度学习模型。该模型通过MSMDC提取多层次特征,结合CBAM动态分配特征权重,并利用BiGRU建模时间序列的双向依赖特性,从而实现对复杂能耗数据的高效建模。实验数据选自摩洛哥得土安市区域建筑2017年全年用电数据,包含环境等多维特征。为评估模型性能,设计消融与对比实验,比较了本文模型与LSTM-Informer和AMTCN等主流方法的表现。实验结果表明,本文模型在MAE、RMSE、MAPE和R2等指标上均优于对比模型,具备良好的预测精度与结构迁移适应性。 展开更多
关键词 区域建筑能耗预测 多尺度多空洞卷积 卷积块注意力机制 双向门控循环单元
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融合DyCNN和CBAM的石墨电极成型VOCs浓度预测模型 认领 引用
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作者 王振方 王子涵 《智能计算机与应用》 2026年第6期17-24,共8页
针对石墨电极成型过程中挥发性有机物(VOCs)排放难以准确预测的问题,提出了一种融合动态卷积神经网络(Dy-CNN)和卷积块注意力模块(CBAM)的TCN-BiGRU混合神经网络预测模型。使用随机森林(RF)算法对影响石墨电极成型VOCs浓度的环境变量进... 针对石墨电极成型过程中挥发性有机物(VOCs)排放难以准确预测的问题,提出了一种融合动态卷积神经网络(Dy-CNN)和卷积块注意力模块(CBAM)的TCN-BiGRU混合神经网络预测模型。使用随机森林(RF)算法对影响石墨电极成型VOCs浓度的环境变量进行权重分析。利用DyCNN增强CBAM对不同特征维度权重的提取和处理能力,并将改进的CBAM模块结合TCN-BiGRU网络捕获VOCs污染物影响因素之间关系和时间序列的长期变化规律。实验结果表明,融合后的DCTCN-BiGRU模型在预测精度和数据拟合度上相较于其他模型具有显著优势,可为炭素企业VOCs治理提供新的技术手段。 展开更多
关键词 VOCs浓度预测 石墨电极成型 动态卷积神经网络(DyCNN) 卷积块注意力模块(CBAM) 随机森林(RF)
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基于CBAM增强与多尺度特征融合的AD MRI图像分类方法 认领 引用
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作者 韦建武 王宋 +3 位作者 任建禹 肖叶子 邵梅 韩刚 《西安邮电大学学报》 2026年第2期109-117,共9页
为解决现有基于深度学习的阿尔茨海默病(Alzheimer's Disease,AD)高分辨率磁共振成像(Magnetic Resonance Imaging,MRI)图像分类方法中全局特征建模不足、多尺度病理信息利用不充分及类别不平衡问题,提出融合卷积块注意力模块与多... 为解决现有基于深度学习的阿尔茨海默病(Alzheimer's Disease,AD)高分辨率磁共振成像(Magnetic Resonance Imaging,MRI)图像分类方法中全局特征建模不足、多尺度病理信息利用不充分及类别不平衡问题,提出融合卷积块注意力模块与多尺度过渡层的改进密集连接网络(DenseNet)模型。具体方法为:在DenseNet121架构下,于各密集块末端集成卷积块注意力模块(Convolutional Block Attention Module,CBAM)以聚焦关键脑区病理变化,设计多尺度Transition层优化下采样时多尺度病理信息融合与保全,采用焦点损失函数缓解类别不平衡。OASIS-1数据集实验显示,该模型分类准确率90.91%、F1值92.12%、召回率94.00%,显著优于MobileNetV2、VGG16及传统DenseNet模型。其能提升AD分期诊断精度,尤其在降低痴呆漏诊率、识别轻度痴呆(黄金干预窗口)上表现突出,可为临床早期干预提供支撑,具备临床转化潜力。 展开更多
关键词 阿尔茨海默病 磁共振成像图像分类 多尺度特征融合 密集连接网络 深度学习 卷积块注意力模块
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基于逐次变分模态分解和CBAM-ResNet的滚动轴承故障诊断方法 认领 引用 被引量：11
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作者 陈志刚 陶子纯 +1 位作者 王衍学 史梦瑶 《振动与冲击》 EI CSCD 北大核心 2025年第4期298-304,312,共7页
针对噪声背景下滚动轴承信号故障特征提取与智能诊断问题,提出基于逐次变分模态分解(successive variational mode decomposition,SVMD)以及注意力机制-残差神经网络(convolutional block attention module-residual neural network,CBA... 针对噪声背景下滚动轴承信号故障特征提取与智能诊断问题,提出基于逐次变分模态分解(successive variational mode decomposition,SVMD)以及注意力机制-残差神经网络(convolutional block attention module-residual neural network,CBAM-ResNet)的轴承故障诊断方法。首先对轴承振动信号进行SVMD分解成一系列本征模态分量,根据包络熵和峭度融合评价指标选择含故障特征明显的模态分量并重构;将重构信号进行短时傅里叶变换得到时频图像。之后利用CBAM能够自适应捕捉图形特征的特点,把重构信号的时频图像输入CBAM-ResNet模型进行特征提取和故障模式识别。在CBAM-ResNet模型训练过程中,使用迁移学习的方法初始化ResNet模型的参数来提高模型的泛化性。与其他传统模型相比,该研究的分类准确率高达96.68%,具有更强的故障特征提取能力。试验结果表明,CBAM-ResNet模型在变工况环境下也具有较高的识别精度。 展开更多
关键词 故障诊断 滚动轴承 逐次变分模态分解 卷积注意力模块 残差神经网络
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基于I_CBAM-DenseNet模型的小麦发育期识别研究 认领 引用 被引量：2
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作者 付景枝 马悦 +4 位作者 宏观 刘云平 吴文宇 丁明明 尹泽凡 《南京信息工程大学学报》 CAS 北大核心 2025年第1期42-52,共11页
针对我国农作物发育期人工观测效率低、识别准确率不高等问题,提出一种基于I_CBAM-DenseNet模型的小麦发育期识别方法.该方法以密集连接卷积网络(DenseNet)为主干提取网络,融入卷积块注意模块CBAM.先将CBAM中的空间注意力模块(SAM)与通... 针对我国农作物发育期人工观测效率低、识别准确率不高等问题,提出一种基于I_CBAM-DenseNet模型的小麦发育期识别方法.该方法以密集连接卷积网络(DenseNet)为主干提取网络,融入卷积块注意模块CBAM.先将CBAM中的空间注意力模块(SAM)与通道注意力模块(CAM)由传统的串联连接改为并行连接,并将改进的CBAM(I_CBAM)插入到DenseNet最后一个密集网络中,构建一种I_CBAM-DenseNet模型,再选取小麦7个重要发育时期进行自动识别.为最大化提取小麦的特征信息,将超绿特征(ExG)因子和最大类间方差法(Otsu)相结合对采集到的小麦图像进行分割处理.对比分析了I_CBAM-DenseNet、AlexNet、ResNet、DenseNet、CBAM-DenseNet以及VGG等模型的准确率和损失值的变化.结果表明,采取基于I_CBAM-DenseNet的卷积神经网络建立的模型,准确率达到99.64%,高于对比模型. 展开更多
关键词 小麦 发育期 DenseNet 卷积块注意模块(CBAM)
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基于CBAM-STCN的齿轮箱故障智能诊断方法 认领 引用 被引量：3
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作者 万志国 王治国 +1 位作者 赵伟 窦益华 《科学技术与工程》 EI 北大核心 2025年第9期3760-3768,共9页
针对齿轮箱在多种工况下故障特征存在差异,故障诊断易受噪声干扰,导致故障诊断模型泛化性差和识别准确率低的问题,提出一种端到端的具有混合注意力机制和软阈值化特点的时间卷积神经网络(convolutional block attention module-sparse t... 针对齿轮箱在多种工况下故障特征存在差异,故障诊断易受噪声干扰,导致故障诊断模型泛化性差和识别准确率低的问题,提出一种端到端的具有混合注意力机制和软阈值化特点的时间卷积神经网络(convolutional block attention module-sparse temporal convolutional network with soft thresholding,CBAM-STCN)齿轮箱故障诊断模型识别分类方法。首先,利用希尔伯特变换将齿轮故障振动信号转换为包络谱信号;然后,将其输入CBAM-STCN故障诊断模型中;该模型嵌入的混合注意力机制模块(convolutional block attention module,CBAM),能够自适应学习通道和空间注意力的权重,提取与故障特征相关的敏感信息;嵌入的软阈值函数能够最小化模型输出和原输入之间的差异;最后,利用所提出的方法对两种工况、不同类型的齿轮故障进行识别分类。结果表明:CBAM-STCN故障诊断模型对齿轮故障智能诊断的平均准确率为98.95%。该方法对于齿轮箱故障的智能诊断具有一定的参考价值。 展开更多
关键词 齿轮箱 故障智能诊断 混合注意力机制 软阈值化 时间卷积神经网络
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基于CBAM-CNN的CPS负荷重分配攻击检测定位方法设计 认领 引用
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作者 陆玲霞 马朝祥 +1 位作者 闫旻睿 于淼 《实验技术与管理》 CAS 北大核心 2025年第6期78-89,共12页
负荷重分配攻击是一种特殊的虚假信息注入攻击。对于电力信息物理系统,基于模型的方法难以检测定位多类型负荷重分配攻击,且针对多类型负荷重分配攻击的数据驱动检测定位方法研究较少。为此,设计了一种以双层规划模型为基础的,基于带卷... 负荷重分配攻击是一种特殊的虚假信息注入攻击。对于电力信息物理系统,基于模型的方法难以检测定位多类型负荷重分配攻击,且针对多类型负荷重分配攻击的数据驱动检测定位方法研究较少。为此,设计了一种以双层规划模型为基础的,基于带卷积注意力模块神经网络的负荷重分配攻击定位检测方法。首先对电力信息物理系统中的信息系统进行建模,总结得到三种信息侧负荷重分配攻击行为。随后建立考虑攻击者和调度中心管理者博弈关系的双层规划模型,针对不同攻击场景生成负荷重分配攻击数据集。为了检测定位不同类型的攻击,将所研究问题转化为多标签分类问题,利用卷积神经网络的卷积结构特性挖掘并学习具有稀疏标签数据的邻域信息,引入卷积注意力模块,从通道信息和空间信息两个角度增强网络对于重点信息的学习能力,改善了网络漏判率较高的问题,提高了网络检测定位性能。在38节点电力信息物理系统算例上进行仿真实验,验证了所提方法的有效性。与对比方法相比,所提方法对于三种攻击类型都有较低的误判率和漏判率,检测定位性能更加出色。 展开更多
关键词 电力信息物理系统 负荷重分配攻击 双层规划模型 数据驱动 卷积注意力模块 卷积神经网络
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Rolling Bearing Fault Diagnosis Based on MTF Encoding and CBAM-LCNN Mechanism 认领 引用 被引量：1
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作者 Wei Liu Sen Liu +2 位作者 Yinchao He Jiaojiao Wang Yu Gu 《Computers, Materials & Continua》 SCIE EI 2025年第3期4863-4880,共18页
To address the issues of slow diagnostic speed,low accuracy,and poor generalization performance in traditional rolling bearing fault diagnosis methods,we propose a rolling bearing fault diagnosis method based on Marko... To address the issues of slow diagnostic speed,low accuracy,and poor generalization performance in traditional rolling bearing fault diagnosis methods,we propose a rolling bearing fault diagnosis method based on Markov Transition Field(MTF)image encoding combined with a lightweight convolutional neural network that integrates a Convolutional Block Attention Module(CBAM-LCNN).Specifically,we first use the Markov Transition Field to convert the original one-dimensional vibration signals of rolling bearings into two-dimensional images.Then,we construct a lightweight convolutional neural network incorporating the convolutional attention module(CBAM-LCNN).Finally,the two-dimensional images obtained from MTF mapping are fed into the CBAM-LCNN network for image feature extraction and fault diagnosis.We validate the effectiveness of the proposed method on the bearing fault datasets from Guangdong University of Petrochemical Technology’s multi-stage centrifugal fan and Case Western Reserve University.Experimental results show that,compared to other advanced baseline methods,the proposed rolling bearing fault diagnosis method offers faster diagnostic speed and higher diagnostic accuracy.In addition,we conducted experiments on the Xi’an Jiaotong University rolling bearing dataset,achieving excellent results in bearing fault diagnosis.These results validate the strong generalization performance of the proposed method.The method presented in this paper not only effectively diagnoses faults in rolling bearings but also serves as a reference for fault diagnosis in other equipment. 展开更多
关键词 Rolling bearing fault diagnosis markov transition field lightweight convolutional neural network convolutional block attention module
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Marine organism classification method based on hierarchical multi-scale attention mechanism 认领 引用 被引量：1
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作者 XU Haotian CHENG Yuanzhi +1 位作者 ZHAO Dong XIE Peidong 《Optoelectronics Letters》 EI 2025年第6期354-361,共8页
We propose a hierarchical multi-scale attention mechanism-based model in response to the low accuracy and inefficient manual classification of existing oceanic biological image classification methods. Firstly, the hie... We propose a hierarchical multi-scale attention mechanism-based model in response to the low accuracy and inefficient manual classification of existing oceanic biological image classification methods. Firstly, the hierarchical efficient multi-scale attention(H-EMA) module is designed for lightweight feature extraction, achieving outstanding performance at a relatively low cost. Secondly, an improved EfficientNetV2 block is used to integrate information from different scales better and enhance inter-layer message passing. Furthermore, introducing the convolutional block attention module(CBAM) enhances the model's perception of critical features, optimizing its generalization ability. Lastly, Focal Loss is introduced to adjust the weights of complex samples to address the issue of imbalanced categories in the dataset, further improving the model's performance. The model achieved 96.11% accuracy on the intertidal marine organism dataset of Nanji Islands and 84.78% accuracy on the CIFAR-100 dataset, demonstrating its strong generalization ability to meet the demands of oceanic biological image classification. 展开更多
关键词 integrate information different scales hierarchical multi scale attention lightweight feature extraction focal loss efficientnetv marine organism classification oceanic biological image classification methods convolutional block attention module
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融合CBAM的违法犯罪类安卓恶意软件检测与分类模型研究 认领 引用 被引量：3
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作者 刘红玉 高见 《计算机工程与应用》 EI CSCD 北大核心 2025年第6期317-327,共11页
针对公安工作领域移动终端APP违法犯罪日益频发的情况,为解决Android恶意违法犯罪软件检测领域中相关数据集数量少、分类不清晰,识别Android恶违法软件可行性方法匮乏等情况,提出了一种基于安卓违法犯罪APP数据集,融合CBAM注意力机制的... 针对公安工作领域移动终端APP违法犯罪日益频发的情况,为解决Android恶意违法犯罪软件检测领域中相关数据集数量少、分类不清晰,识别Android恶违法软件可行性方法匮乏等情况,提出了一种基于安卓违法犯罪APP数据集,融合CBAM注意力机制的深度学习模型。收集6181个违法犯罪类APP并整理划分为4个家族;对违法APP软件进行灰度图、RGB以及RGBA三种图像可视化处理;利用融合CBAM注意力机制的深度模型进行家族检测分类。在违法犯罪APP数据集上的实验表明,融合CBAM机制的Resnet18模型在RGBA图像上与未引入该机制的灰度图图像相比,准确度提升了4.04%,达到93.52%。融合CBAM机制的模型在公开Drebin数据集上进行了验证,引入CBAM深度学习模型VGG16在RGBA图像上取得了96.35%的准确率。 展开更多
关键词 违法犯罪 安卓恶意软件 RGBA图像 可视化处理 卷积块注意力模块(CBAM) 深度学习
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结合ResNet和CBAM的静态图像行为识别方法 认领 引用 被引量：3
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作者 高晗 万方杰 马明旭 《郑州大学学报（理学版）》 CAS 北大核心 2025年第3期65-71,共7页
针对静态图像行为识别缺乏大规模训练数据集和无法利用时空特征所导致的识别效果不佳问题,提出一种结合残差神经网络(residual neural network,ResNet)和卷积注意力模块(convolutional block attention module,CBAM)的静态图像行为识别... 针对静态图像行为识别缺乏大规模训练数据集和无法利用时空特征所导致的识别效果不佳问题,提出一种结合残差神经网络(residual neural network,ResNet)和卷积注意力模块(convolutional block attention module,CBAM)的静态图像行为识别方法。使用特定数据增强技术对数据集进行扩充,采用迁移学习方法对模型初始化,并进行微调训练提升对静态图像行为识别的特征表达能力。通过将CBAM嵌入ResNet的第1个卷积层后来调整模型注意力,利用Grad-CAM方法提取模型识别图像时关注区域并进行可视化,对精度提升进行了解释。在PPMI数据集上,所提方法在演奏乐器类、持有乐器类和总类的平均识别精度分别达到88.30%、81.94%和77.93%,验证了方法的有效性。 展开更多
关键词 残差网络 行为识别 卷积注意力模块 静态图像 迁移学习
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基于CBAM-GLU-ISF的多模态融合恶意软件检测方法 认领 引用 被引量：2
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作者 彭飞鸿 刘万平 黄东 《计算机工程与应用》 EI CSCD 北大核心 2025年第20期306-314,共9页
恶意软件普遍结合代码混淆技术,基于单特征的检测方法特征信息受限,相对多特征检测方法存在检测准确率偏低的问题。不同模态之间潜在的关联性信息可以提高检测效果上限,目前的多特征检测方法在特征融合时缺乏对特征之间关联性的建模,导... 恶意软件普遍结合代码混淆技术,基于单特征的检测方法特征信息受限,相对多特征检测方法存在检测准确率偏低的问题。不同模态之间潜在的关联性信息可以提高检测效果上限,目前的多特征检测方法在特征融合时缺乏对特征之间关联性的建模,导致其检测准确率欠佳。为了更全面地表征恶意软件并提高检测精度,提出一种基于卷积神经网络CNN和门控线性单元GLU的多模态融合恶意软件检测方法CBAM-GLU-ISF。以恶意软件两种模态:灰度图和字节序列为分析对象,在卷积神经网络中添加卷积块注意力模块(CBAM),结合通道注意力和空间注意力实现对灰度图的关键特征提取。字节序列是软件在计算机上最直接的表示,门控线性单元结合加性注意力机制(additive attention)在高效地捕获长序列依赖关系的基础上,实现对字节序列关键特征的提取。多模态特征融合模块(ISF)对并行特征提取网络的两种模态特征进行融合,挖掘利用两种模态特征之间存在的关联性信息,将恶意软件表征为一个更全面的多模态特征。最后,通过检测层完成恶意软件识别。实验结果表明,所提方法检测准确率达到99.1%,AUC达到了99.8%,对比现有工作中的单特征和多特征检测算法有明显提升,验证了该方法的有效性。 展开更多
关键词 恶意软件检测 多模态融合 卷积神经网络(CNN) 卷积块注意力模块 门控线性单元(GLU)
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基于CNN和CBAM的钢材表面缺陷检测研究 认领 引用 被引量：1
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作者 徐吉高 陈江义 +1 位作者 秦东晨 沈鹏 《机械设计与制造》 北大核心 2025年第2期149-152,共4页
为了针对钢材表面缺陷尺度小、有用的特征信息少,提出了在卷积神经网络(CNN)的基础架构上添加卷积注意力模块(CBAM),改进后的网络模型对于数据集进行更层次的特征提取,更加关注特征信息的位置以及意义,使提取到的特征更加精炼,加强特征... 为了针对钢材表面缺陷尺度小、有用的特征信息少,提出了在卷积神经网络(CNN)的基础架构上添加卷积注意力模块(CBAM),改进后的网络模型对于数据集进行更层次的特征提取,更加关注特征信息的位置以及意义,使提取到的特征更加精炼,加强特征重复利用,提升模型的表现力。实验结果表明:在数据集特征信息少、分类对象较为单一的情况下,本文所提出方法相较于更深层次的网络模型对于分类任务取得更好的效果,缺陷检测分类准确率可达99%,平均检测速度8.21ms,满足实时检测需求。 展开更多
关键词 钢材表面缺陷检测 卷积神经网络 卷积注意力模块
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基于CBAM的元学习小样本变工况轴承故障诊断 认领 引用 被引量：2
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作者 朱怡琳 库鹏博 张守京 《轻工机械》 CAS 2025年第1期55-62,71,共8页
针对在实际生产中轴承故障数据的样本量少、不同故障类别样本分布不均衡等影响轴承故障诊断的问题,课题组提出了一种基于注意力机制的元学习方法。首先通过连续小波变换将一维振动信号转化为二维图像,并将二维图像作为网络的输入,然后... 针对在实际生产中轴承故障数据的样本量少、不同故障类别样本分布不均衡等影响轴承故障诊断的问题,课题组提出了一种基于注意力机制的元学习方法。首先通过连续小波变换将一维振动信号转化为二维图像,并将二维图像作为网络的输入,然后采用卷积注意力模块(Convolutional Block Attention Module,CBAM)预训练特征提取器和分类器对大规模已知轴承故障的轴承数据进行预训练,增强网络对时频图特征的表示能力和分类性能,从而提高轴承故障诊断的准确性和鲁棒性;考虑了多种工况在CWRU数据集上进行交叉验证。结果显示该方法在小样本故障诊断的1-shot和5-shot任务上优于其他故障诊断方法,表明所提方法具有较高的鲁棒性和泛化性。 展开更多
关键词 轴承 故障诊断 小样本 变工况 元学习 卷积注意力模块
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Transmission Facility Detection with Feature-Attention Multi-Scale Robustness Network and Generative Adversarial Network 认领 引用
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作者 Yunho Na Munsu Jeon +4 位作者 Seungmin Joo Junsoo Kim Ki-Yong Oh Min Ku Kim Joon-Young Park 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第7期1013-1044,共32页
This paper proposes an automated detection framework for transmission facilities using a featureattention multi-scale robustness network(FAMSR-Net)with high-fidelity virtual images.The proposed framework exhibits thre... This paper proposes an automated detection framework for transmission facilities using a featureattention multi-scale robustness network(FAMSR-Net)with high-fidelity virtual images.The proposed framework exhibits three key characteristics.First,virtual images of the transmission facilities generated using StyleGAN2-ADA are co-trained with real images.This enables the neural network to learn various features of transmission facilities to improve the detection performance.Second,the convolutional block attention module is deployed in FAMSR-Net to effectively extract features from images and construct multi-dimensional feature maps,enabling the neural network to perform precise object detection in various environments.Third,an effective bounding box optimization method called Scylla-IoU is deployed on FAMSR-Net,considering the intersection over union,center point distance,angle,and shape of the bounding box.This enables the detection of power facilities of various sizes accurately.Extensive experiments demonstrated that FAMSRNet outperforms other neural networks in detecting power facilities.FAMSR-Net also achieved the highest detection accuracy when virtual images of the transmission facilities were co-trained in the training phase.The proposed framework is effective for the scheduled operation and maintenance of transmission facilities because an optical camera is currently the most promising tool for unmanned aerial vehicles.This ultimately contributes to improved inspection efficiency,reduced maintenance risks,and more reliable power delivery across extensive transmission facilities. 展开更多
关键词 Object detection virtual image transmission facility convolutional block attention module Scylla-IoU
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