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基于DA-ResCNN的加密流量分类方法 认领 引用
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作者 潘治权 邬九连 +2 位作者 张铭哲 张维立 纪祥敏 《北京信息科技大学学报（自然科学版）》 2026年第2期73-80,共8页
为了应对加密流量分类中的特征维度高、类别分布复杂和判别难度大等挑战,提出了一种基于双重注意力残差卷积神经网络(dual-attention residual convolutional neural network,DA-ResCNN)的加密流量分类方法。该方法结合残差学习和一维... 为了应对加密流量分类中的特征维度高、类别分布复杂和判别难度大等挑战,提出了一种基于双重注意力残差卷积神经网络(dual-attention residual convolutional neural network,DA-ResCNN)的加密流量分类方法。该方法结合残差学习和一维卷积神经网络(one-dimensional convolutional neural network,1D-CNN),并引入高效通道注意力(efficient channel attention,ECA)和卷积块注意力模块(convolutional block attention module,CBAM),先进行通道重标定,再聚焦空间特征,从而在保持模型轻量化的同时增强时空联合建模能力。残差捷径连接保证了深层网络的可训练性,并有效稳定了梯度流;注意力机制则动态调整关键通道和位置的权重,减少了相似类别的混淆。实验结果表明,DA-ResCNN模型较1D-CNN模型在ISCX VPN-nonVPN数据集上的分类准确率提高了10.3百分点,在Email、Chat、VPN-VoIP类别上的F1值分别提高了28.0、33.0和11.0百分点。 展开更多
关键词 网络流量分类 双重注意力 残差网络 高效通道注意力(efficient channel attention,ECA) 卷积块注意力模块(convolutional block attention module,CBAM)
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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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Research on YOLO algorithm for lightweight PCB defect detection based on MobileViT 认领 引用 被引量：2
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作者 LIU Yuchen LIU Fuzheng JIANG Mingshun 《Optoelectronics Letters》 EI 2025年第8期483-490,共8页
Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order t... Current you only look once(YOLO)-based algorithm model is facing the challenge of overwhelming parameters and calculation complexity under the printed circuit board(PCB)defect detection application scenario.In order to solve this problem,we propose a new method,which combined the lightweight network mobile vision transformer(Mobile Vi T)with the convolutional block attention module(CBAM)mechanism and the new regression loss function.This method needed less computation resources,making it more suitable for embedded edge detection devices.Meanwhile,the new loss function improved the positioning accuracy of the bounding box and enhanced the robustness of the model.In addition,experiments on public datasets demonstrate that the improved model achieves an average accuracy of 87.9%across six typical defect detection tasks,while reducing computational costs by nearly 90%.It significantly reduces the model's computational requirements while maintaining accuracy,ensuring reliable performance for edge deployment. 展开更多
关键词 YOLO lightweight network mobile vision transformer mobile Lightweight Network convolutional block attention module cbam mechanism MobileViT CBAM PCB Defect Detection Regression Loss Function
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Attention-Augmented YOLOv8 with Ghost Convolution for Real-Time Vehicle Detection in Intelligent Transportation Systems 认领 引用
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作者 Syed Sajid Ullah Muhammad Zunair Zamir +1 位作者 Ahsan Ishfaq Salman Khan 《Journal on Artificial Intelligence》 2025年第1期255-274,共20页
Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional B... Accurate vehicle detection is essential for autonomous driving,traffic monitoring,and intelligent transportation systems.This paper presents an enhanced YOLOv8n model that incorporates the Ghost Module,Convolutional Block Attention Module(CBAM),and Deformable Convolutional Networks v2(DCNv2).The Ghost Module streamlines feature generation to reduce redundancy,CBAM applies channel and spatial attention to improve feature focus,and DCNv2 enables adaptability to geometric variations in vehicle shapes.These components work together to improve both accuracy and computational efficiency.Evaluated on the KITTI dataset,the proposed model achieves 95.4%mAP@0.5—an 8.97% gain over standard YOLOv8n—along with 96.2% precision,93.7% recall,and a 94.93%F1-score.Comparative analysis with seven state-of-the-art detectors demonstrates consistent superiority in key performance metrics.An ablation study is also conducted to quantify the individual and combined contributions of GhostModule,CBAM,and DCNv2,highlighting their effectiveness in improving detection performance.By addressing feature redundancy,attention refinement,and spatial adaptability,the proposed model offers a robust and scalable solution for vehicle detection across diverse traffic scenarios. 展开更多
关键词 YOLOv8n vehicle detection deformable convolutional networks(DCNv2) ghost module convolutional block attention module(CBAM) attention mechanisms
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基于注意力特征融合的SqueezeNet细粒度图像分类模型 认领 引用 被引量：8
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作者 李明悦 何乐生 +1 位作者 雷晨 龚友梅 《云南大学学报（自然科学版）》 CAS CSCD 北大核心 2021年第5期868-876,共9页
针对现有细粒度图像分类算法普遍存在的模型结构复杂、参数多、分类准确率较低等问题,提出一种注意力特征融合的SqueezeNet细粒度图像分类模型.通过对现有细粒度图像分类算法和轻量级卷积神经网络的分析,首先使用3个典型的预训练轻量级... 针对现有细粒度图像分类算法普遍存在的模型结构复杂、参数多、分类准确率较低等问题,提出一种注意力特征融合的SqueezeNet细粒度图像分类模型.通过对现有细粒度图像分类算法和轻量级卷积神经网络的分析,首先使用3个典型的预训练轻量级卷积神经网络,对其微调后在公开的细粒度图像数据集上进行验证,经比较后选择了模型性能最佳的SqueezeNet作为图像的特征提取器;然后将两个具有注意力机制的卷积模块嵌入至SqueezeNet网络的每个Fire模块;接着提取出改进后的SqueezeNet的中间层特征进行双线性融合形成新的注意力特征图,与网络的全局特征再融合后分类;最后通过实验对比和可视化分析,网络嵌入Convolution Block Attention Module(CBAM)模块的分类准确率在鸟类、汽车、飞机数据集上依次提高了8.96%、4.89%和5.85%,嵌入Squeeze-and-Excitation(SE)模块的分类准确率依次提高了9.81%、4.52%和2.30%,且新模型在参数量、运行效率等方面比现有算法更具优势. 展开更多
关键词 细粒度图像分类 轻量级卷积神经网络 SqueezeNet 注意力机制 Convolution Block Attention Module(CBAM) Squeeze-and-Excitation(SE) 特征融合
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Hybrid CBAM-EfficientNetV2 Fire Image Recognition Method with Label Smoothing in Detecting Tiny Targets 认领 引用 被引量：1
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作者 Bo Wang Guozhong Huang +3 位作者 Haoxuan Li Xiaolong Chen Lei Zhang Xuehong Gao 《Machine Intelligence Research》 EI CSCD 2024年第6期1145-1161,共17页
Image fire recognition is of great significance in fire prevention and loss reduction through early fire detection and warning.Aiming at the problems of low accuracy of existing fire recognition and high error rate of... Image fire recognition is of great significance in fire prevention and loss reduction through early fire detection and warning.Aiming at the problems of low accuracy of existing fire recognition and high error rate of tiny target detection,this study proposed a fire recognition model based on a channel space attention mechanism.First,the convolutional block attention module(CBAM)is intro-duced into the first and last convolutional layers EfficientNetV2,which shows strong feature extraction ability and high computational efficiency as the backbone network.In terms of channel and space aspects,the weights in the feature layer are increased,which enhances the semantic information of flame smoke features and makes the model pay more attention to the feature information of fire images.Then,label smoothing based on the cross-entropy loss function is introduced into this study to avoid predicting labels too confidently in the training process to improve the generalization ability of the recognition model.The experimental results show that the fire image re-cognition accuracy based on the CBAM-EfficientNetV2 model reaches 98.9%.The accuracy of smoke image recognition can reach 98.5%.The accuracy of small target detection can reach 96.1%.At the same time,we compared the existing methods and found that the proposed method achieved higher accuracy,precision,recall,and F1-score.Finally,the fire image results are visualized using the Grad-CAM technique,which makes the model more effective and more intuitive in detecting tiny targets. 展开更多
关键词 Fire recognition tiny target detection efficientNetV2 label smoothing convolutional block attention module(CBAM)
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