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
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
滚动轴承的工作状况关系到使用滚动轴承的机械能否正常运行,预测轴承的剩余使用寿命(RUL)是避免机械系统失效的关键。针对传统的轴承使用寿命预测方法无法自适应调节特征权重、提取有用特征,造成预测值误差过大的问题,提出了一种带有卷...滚动轴承的工作状况关系到使用滚动轴承的机械能否正常运行,预测轴承的剩余使用寿命(RUL)是避免机械系统失效的关键。针对传统的轴承使用寿命预测方法无法自适应调节特征权重、提取有用特征,造成预测值误差过大的问题,提出了一种带有卷积块注意力模块(CBAM)的动态残差网络(Dy Res Net)用于预测轴承RUL。对振动信号进行快速傅里叶变换求得频域累积幅值特征,在动态残差网络中加入CBAM模块,并利用压缩激励模块进行特征细化得出预测结果,使用公开轴承数据集对所提模型进行评估。实验表明:与其他模型相比,Dy Res Net-CBAM模型能够充分提取特征信息,对轴承RUL预测的准确度高于其他模型。展开更多
针对静态人群图像中背景干扰和尺度变化等问题,采用多尺度特征提取模块(Res2Net)以更细的粒度提取多尺度特征,提高对不同尺寸人头的计数性能;引入卷积注意力模块(CBAM),分别在通道域和空间域上提高人群区域的权重,有效改善了高密度和复...针对静态人群图像中背景干扰和尺度变化等问题,采用多尺度特征提取模块(Res2Net)以更细的粒度提取多尺度特征,提高对不同尺寸人头的计数性能;引入卷积注意力模块(CBAM),分别在通道域和空间域上提高人群区域的权重,有效改善了高密度和复杂的人群场景下背景干扰等问题。在此基础上,将CBAM模块集成到Res2Net模块中,形成了新的多尺度特征提取模块CBAM-Res2Net。在后端网络中设计了一个扩张模块以提取更深层的特征并进行特征融合回归,从而生成高质量的密度图。并且分别在ShanghaiTech Part A、ShanghaiTech Part B和UCF_CC_50数据集上进行了算法对比实验,本文模型在上述数据集的平均绝对误差和均方根误差分别为61.4、7.3、255.6和98.5、10.8、310.2,综合性能均优于其他算法,验证了模型的准确性和鲁棒性。展开更多
文摘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.
文摘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.
文摘滚动轴承的工作状况关系到使用滚动轴承的机械能否正常运行,预测轴承的剩余使用寿命(RUL)是避免机械系统失效的关键。针对传统的轴承使用寿命预测方法无法自适应调节特征权重、提取有用特征,造成预测值误差过大的问题,提出了一种带有卷积块注意力模块(CBAM)的动态残差网络(Dy Res Net)用于预测轴承RUL。对振动信号进行快速傅里叶变换求得频域累积幅值特征,在动态残差网络中加入CBAM模块,并利用压缩激励模块进行特征细化得出预测结果,使用公开轴承数据集对所提模型进行评估。实验表明:与其他模型相比,Dy Res Net-CBAM模型能够充分提取特征信息,对轴承RUL预测的准确度高于其他模型。
文摘针对静态人群图像中背景干扰和尺度变化等问题,采用多尺度特征提取模块(Res2Net)以更细的粒度提取多尺度特征,提高对不同尺寸人头的计数性能;引入卷积注意力模块(CBAM),分别在通道域和空间域上提高人群区域的权重,有效改善了高密度和复杂的人群场景下背景干扰等问题。在此基础上,将CBAM模块集成到Res2Net模块中,形成了新的多尺度特征提取模块CBAM-Res2Net。在后端网络中设计了一个扩张模块以提取更深层的特征并进行特征融合回归,从而生成高质量的密度图。并且分别在ShanghaiTech Part A、ShanghaiTech Part B和UCF_CC_50数据集上进行了算法对比实验,本文模型在上述数据集的平均绝对误差和均方根误差分别为61.4、7.3、255.6和98.5、10.8、310.2,综合性能均优于其他算法,验证了模型的准确性和鲁棒性。
