目前,无监督单模态行人重识别研究主要集中于可见光图像。随着新型红外摄像头的普及,无监督红外行人重识别也展现出其研究价值。由于红外图像对比度低、缺乏颜色纹理细节信息,因此全局信息对于红外行人重识别至关重要。本文设计了基于F-...目前,无监督单模态行人重识别研究主要集中于可见光图像。随着新型红外摄像头的普及,无监督红外行人重识别也展现出其研究价值。由于红外图像对比度低、缺乏颜色纹理细节信息,因此全局信息对于红外行人重识别至关重要。本文设计了基于F-ResGAM的无监督红外行人重识别网络。该网络首先利用小波变换对图像进行预处理以增强特征提取能力,接着在resnet50网络结构中引入全局注意力机制(Global Attention Mechanism,GAM)关注更多的全局信息。此外,由于红外伪标签噪声较大,本文提出采用基于样本扩展的分组采样(Group Sampling based on Sample Expansion,GSSE)策略进一步优化伪标签生成,从而提升了模型的识别精度。实验结果表明,本文提出的优化方法有效提升了无监督红外行人重识别的精度,尤其是rank指标显著提升。展开更多
车辆检测是智能交通系统和自动驾驶的重要组成部分。然而,实际交通场景中存在许多不确定因素,导致车辆检测模型的准确率低实时性差。为了解决这个问题,提出了一种快速准确的车辆检测算法——YOLOv8-DEL。使用DGCST(dynamic group convol...车辆检测是智能交通系统和自动驾驶的重要组成部分。然而,实际交通场景中存在许多不确定因素,导致车辆检测模型的准确率低实时性差。为了解决这个问题,提出了一种快速准确的车辆检测算法——YOLOv8-DEL。使用DGCST(dynamic group convolution shuffle transformer)模块代替C2f模块来重构主干网络,以增强特征提取能力并使网络更轻量;添加的P2检测层能使模型更敏锐地定位和检测小目标,同时采用Efficient RepGFPN进行多尺度特征融合,以丰富特征信息并提高模型的特征表达能力;通过结合GroupNorm和共享卷积的优点,设计了一种轻量型共享卷积检测头,在保持精度的前提下,有效减少参数量并提升检测速度。与YOLOv8相比,提出的YOLOv8-DEL在BDD100K数据集和KITTI数据集上,mAP@0.5分别提高了4.8个百分点和1.2个百分点,具有实时检测速度(208.6 FPS和216.4 FPS),在检测精度和速度方面实现了更有利的折中。展开更多
To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied...To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.展开更多
文摘目前,无监督单模态行人重识别研究主要集中于可见光图像。随着新型红外摄像头的普及,无监督红外行人重识别也展现出其研究价值。由于红外图像对比度低、缺乏颜色纹理细节信息,因此全局信息对于红外行人重识别至关重要。本文设计了基于F-ResGAM的无监督红外行人重识别网络。该网络首先利用小波变换对图像进行预处理以增强特征提取能力,接着在resnet50网络结构中引入全局注意力机制(Global Attention Mechanism,GAM)关注更多的全局信息。此外,由于红外伪标签噪声较大,本文提出采用基于样本扩展的分组采样(Group Sampling based on Sample Expansion,GSSE)策略进一步优化伪标签生成,从而提升了模型的识别精度。实验结果表明,本文提出的优化方法有效提升了无监督红外行人重识别的精度,尤其是rank指标显著提升。
文摘To address the problem that dynamic wind turbine clutter(WTC)significantly degrades the performance of weather radar,a WTC mitigation algorithm using morphological component analysis(MCA)with group sparsity is studied in this paper.The ground clutter is suppressed firstly to reduce the morphological compositions of radar echo.After that,the MCA algorithm is applied and the window used in the short-time Fourier transform(STFT)is optimized to lessen the spectrum leakage of WTC.Finally,the group sparsity structure of WTC in the STFT domain can be utilized to decrease the degrees of freedom in the solution,thus contributing to better estimation performance of weather signals.The effectiveness and feasibility of the proposed method are demonstrated by numerical simulations.
