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
为了降低服装目标检测模型的参数量和浮点型计算量,提出一种改进的轻量级服装目标检测模型——GYOLOv5s.首先使用Ghost卷积重构YOLOv5s的主干网络;然后使用DeepFashion2数据集中的部分数据进行模型训练和验证;最后将训练好的模型用于服...为了降低服装目标检测模型的参数量和浮点型计算量,提出一种改进的轻量级服装目标检测模型——GYOLOv5s.首先使用Ghost卷积重构YOLOv5s的主干网络;然后使用DeepFashion2数据集中的部分数据进行模型训练和验证;最后将训练好的模型用于服装图像的目标检测.实验结果表明,G-YOLOv5s的mAP达到71.7%,模型体积为9.09 MB,浮点型计算量为9.8 G FLOPs,与改进前的YOLOv5s网络相比,模型体积压缩了34.8%,计算量减少了41.3%,精度仅下降1.3%,方便部署在资源有限的设备中使用.展开更多
Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the ...Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method.展开更多
针对现有集装箱编号识别算法结构复杂以及难以应用在低成本设备上的问题,文中提出一种基于深度学习的轻量型算法YOLOv4-GSE(You Only Look Once version 4-Ghost-SPPFA-Effective),利用Ghost-Enet网络替代YOLOv4的主干特征提取网络,使用...针对现有集装箱编号识别算法结构复杂以及难以应用在低成本设备上的问题,文中提出一种基于深度学习的轻量型算法YOLOv4-GSE(You Only Look Once version 4-Ghost-SPPFA-Effective),利用Ghost-Enet网络替代YOLOv4的主干特征提取网络,使用Ghost卷积替换所有3×3卷积方式来削减模型的参数量。在加强特征提取部分,提出一种改进后的SPPFA(Spatial Pyramid Pooling with Feature Aggregation)模块来解决由于连续最大池化操作造成的信息丢失问题。添加CBAM(Convolutional Block Attention Module)注意力机制模块对不同通道和空间进行权重分析,增强模型的特征提取能力。相较于YOLOv4,所提算法在集装箱数据集上的mAP(mean Average Precision)值提升了1.02%,参数量减少了91.95%,FLOPs(Floating-point Operations Per Second)减少了94.62%。展开更多
在无线通信环境中压制式复合干扰信号对通信系统的正常工作有着严重的影响,针对其特征提取和识别较为困难的问题,提出一种基于短时傅里叶变换(short time Fourier transform,STFT)和残差卷积网络的复合干扰识别算法。该算法将STFT得到...在无线通信环境中压制式复合干扰信号对通信系统的正常工作有着严重的影响,针对其特征提取和识别较为困难的问题,提出一种基于短时傅里叶变换(short time Fourier transform,STFT)和残差卷积网络的复合干扰识别算法。该算法将STFT得到的时频域信息作为输入,同时对复合干扰信号的种类和干噪比进行识别,为了使模型更加适合部署在移动端上,采用幻影卷积代替普通卷积。仿真结果表明,在干噪比为-15~10 dB的范围内,该算法在5种单一干扰及其复合而成的10种复合干扰信号种类识别任务上准确率可以达到99.97%,在干噪比识别任务上准确率可以达到99.04%。相比于残差卷积网络,该算法在几乎不降低准确率的前提下可以使模型参数量减小38.4%,计算复杂度降低46.6%,更加符合移动端的要求。展开更多
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.
文摘为了降低服装目标检测模型的参数量和浮点型计算量,提出一种改进的轻量级服装目标检测模型——GYOLOv5s.首先使用Ghost卷积重构YOLOv5s的主干网络;然后使用DeepFashion2数据集中的部分数据进行模型训练和验证;最后将训练好的模型用于服装图像的目标检测.实验结果表明,G-YOLOv5s的mAP达到71.7%,模型体积为9.09 MB,浮点型计算量为9.8 G FLOPs,与改进前的YOLOv5s网络相比,模型体积压缩了34.8%,计算量减少了41.3%,精度仅下降1.3%,方便部署在资源有限的设备中使用.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11704221,11574178,and 61675115)the Taishan Scholar Project of Shandong Province,China(Grant No.tsqn201812059)。
文摘Computational ghost imaging(CGI)provides an elegant framework for indirect imaging,but its application has been restricted by low imaging performance.Herein,we propose a novel approach that significantly improves the imaging performance of CGI.In this scheme,we optimize the conventional CGI data processing algorithm by using a novel compressed sensing(CS)algorithm based on a deep convolution generative adversarial network(DCGAN).CS is used to process the data output by a conventional CGI device.The processed data are trained by a DCGAN to reconstruct the image.Qualitative and quantitative results show that this method significantly improves the quality of reconstructed images by jointly training a generator and the optimization process for reconstruction via meta-learning.Moreover,the background noise can be eliminated well by this method.
文摘针对现有集装箱编号识别算法结构复杂以及难以应用在低成本设备上的问题,文中提出一种基于深度学习的轻量型算法YOLOv4-GSE(You Only Look Once version 4-Ghost-SPPFA-Effective),利用Ghost-Enet网络替代YOLOv4的主干特征提取网络,使用Ghost卷积替换所有3×3卷积方式来削减模型的参数量。在加强特征提取部分,提出一种改进后的SPPFA(Spatial Pyramid Pooling with Feature Aggregation)模块来解决由于连续最大池化操作造成的信息丢失问题。添加CBAM(Convolutional Block Attention Module)注意力机制模块对不同通道和空间进行权重分析,增强模型的特征提取能力。相较于YOLOv4,所提算法在集装箱数据集上的mAP(mean Average Precision)值提升了1.02%,参数量减少了91.95%,FLOPs(Floating-point Operations Per Second)减少了94.62%。
文摘在无线通信环境中压制式复合干扰信号对通信系统的正常工作有着严重的影响,针对其特征提取和识别较为困难的问题,提出一种基于短时傅里叶变换(short time Fourier transform,STFT)和残差卷积网络的复合干扰识别算法。该算法将STFT得到的时频域信息作为输入,同时对复合干扰信号的种类和干噪比进行识别,为了使模型更加适合部署在移动端上,采用幻影卷积代替普通卷积。仿真结果表明,在干噪比为-15~10 dB的范围内,该算法在5种单一干扰及其复合而成的10种复合干扰信号种类识别任务上准确率可以达到99.97%,在干噪比识别任务上准确率可以达到99.04%。相比于残差卷积网络,该算法在几乎不降低准确率的前提下可以使模型参数量减小38.4%,计算复杂度降低46.6%,更加符合移动端的要求。
