针对基于You Only Look Once v2算法的目标检测存在精度低及稳健性差的问题,提出一种车辆目标实时检测的You Only Look Once v2优化算法;该算法以You Only Look Once v2算法为基础,通过增加网络深度,增强特征提取能力,同时,通过添加残...针对基于You Only Look Once v2算法的目标检测存在精度低及稳健性差的问题,提出一种车辆目标实时检测的You Only Look Once v2优化算法;该算法以You Only Look Once v2算法为基础,通过增加网络深度,增强特征提取能力,同时,通过添加残差模块,解决网络深度增加带来的梯度消失或弥散问题;该方法将网络结构中低层特征与高层特征进行融合,提升对小目标车辆的检测精度。结果表明,通过在KITTI数据集上进行测试,优化后的算法在检测速度不变的情况下,提高了车辆目标检测精度,平均精度达到0.94,同时提升了小目标检测的准确性。展开更多
OBJECTIVE Mu-Xiang-You-Fang(MXYF)is a classic prescription of Hui medicine,composed of five herbs,which has been used to treat ischemic stroke for many years.However,the potential pharmacological mecha⁃nisms of MXYF r...OBJECTIVE Mu-Xiang-You-Fang(MXYF)is a classic prescription of Hui medicine,composed of five herbs,which has been used to treat ischemic stroke for many years.However,the potential pharmacological mecha⁃nisms of MXYF remain unclear.The present research is to investigate the neuroprotective effect of MXYF and its role in modulating autophagy via AMPK/mTOR signaling pathway in the PC12 oxygen-glucose deprivation and reperfusion(OGD/R)injury model.METHODS MXYF was extracted by supercritical CO2 fluid extraction apparatus.PC12 OGD/R injury model was established by oxygen-glucose deprivation for 2 h and reperfusion for 24 h.The effects of MXYF on the viability and cytotoxicity of PC12 cells were determined through cell counting kit(CCK-8)assay.Colorimetric method was performed to determine the LDH leakage rate.The calcium concentration was determined by chemical fluorescence method and the mitochondrial membrane potential was determined through flow cytometry.Monodansylcadaverine(MDC)staining was conducted to detect autophagosome formation.The expression of LC3,Beclin1,p62,p-AMPK,ULK1,p-mTOR and p-p70s6k proteins were determined by immunofluorescence and Western blotting analyses.RESULTS MXYF(1,2 and 4 mg·L^-1)could significantly increase the cell viability and mitochondrial membrane potential,while decreased the release of lactate dehydrogenase(LDH)and calcium concentration in PC12 cells.Mechanistic studies showed that MXYF reduced the LC3-II/LC3-I ratio and inhibited the expression of beclin1,p-AMPK and ULK1.In comparison,the expres⁃sion of p-mTOR,p-p70s6k and p62 were significantly enhanced.CONCLUSION MXYF inhibits autophagy after OGD/Rinduced PC12 cell injury through AMPK-mTOR pathway,thus MXYF might have therapeutic potential for treating the ischemic stroke.展开更多
为了解决施工场景下安全帽佩戴检测时,由于人员密集、遮挡和复杂背景等原因造成的小目标漏检和错检的问题,提出一种基于YOLOv8n的双重注意力机制的跨层多尺度安全帽佩戴检测算法。首先,设计微小目标检测头,以提高模型对小目标的检测能力...为了解决施工场景下安全帽佩戴检测时,由于人员密集、遮挡和复杂背景等原因造成的小目标漏检和错检的问题,提出一种基于YOLOv8n的双重注意力机制的跨层多尺度安全帽佩戴检测算法。首先,设计微小目标检测头,以提高模型对小目标的检测能力;其次,在特征提取网络中嵌入双重注意力机制,从而更加关注复杂场景下目标信息的特征捕获;然后,将特征融合网络替换成重参数化泛化特征金字塔网络(RepGFPN)改进后的跨层多尺度特征融合结构S-GFPN(Selective layer Generalized Feature Pyramid Network),以实现小目标特征层信息和其他特征层的多尺度融合,并建立长期的依赖关系,从而抑制背景信息的干扰;最后,采用MPDIOU(Intersection Over Union with Minimum Point Distance)损失函数来解决尺度变化不敏感的问题。在公开数据集GDUT-HWD上的实验结果表明,改进后的模型比YOLOv8n的mAP@0.5提升了3.4个百分点,对蓝色、黄色、白色和红色安全帽的检测精度分别提升了2.0、1.1、4.6和9.1个百分点,在密集、遮挡、小目标、反光和黑暗这5类复杂场景下的可视化检测效果也优于YOLOv8n,为实际施工场景中安全帽佩戴检测提供了一种有效方法。展开更多
针对目前遥感图像小目标检测任务中易出现漏检和误检的问题,提出一种SCS-YOLO[SMCA+CSC+SIoU(shape-aware intersection over union loss)-you only look once]的遥感图像小目标检测算法。首先,针对遥感图像中目标小而聚集的问题,构建...针对目前遥感图像小目标检测任务中易出现漏检和误检的问题,提出一种SCS-YOLO[SMCA+CSC+SIoU(shape-aware intersection over union loss)-you only look once]的遥感图像小目标检测算法。首先,针对遥感图像中目标小而聚集的问题,构建空间多尺度卷积注意力(spatial multi-scale convolutional attention,SMCA),提升模型对空间和通道信息的特征提取能力;其次,针对深层网络传递时小目标语义信息容易丢失的问题,设计聚合亚像素卷积(concentrated sub-pixel convolution,CSC),采用多尺度聚合特征提取方法,增强了网络对语义信息的提取能力;最后,将SIoU损失函数替代原模型中的CIoU(complete intersection over union loss)损失函数,加快了网络的收敛速度。SCS-YOLO模型在RSOD和NWPU VHR-10数据集上,平均精确率的平均值(mAP)分别达到97%和90.9%,相较于原模型分别提升了2.2%和2.7%,可见该方法在遥感图像小目标检测任务中的有效性。展开更多
针对交通路口图像复杂,小目标难测且目标之间易遮挡以及天气和光照变化引发的颜色失真、噪声和模糊等问题,提出一种基于YOLOv9(You Only Look Once version 9)的交通路口图像的多目标检测算法ITD-YOLOv9(Intersection Target Detection-...针对交通路口图像复杂,小目标难测且目标之间易遮挡以及天气和光照变化引发的颜色失真、噪声和模糊等问题,提出一种基于YOLOv9(You Only Look Once version 9)的交通路口图像的多目标检测算法ITD-YOLOv9(Intersection Target Detection-YOLOv9)。首先,设计CoT-CAFRNet(Chain-of-Thought prompted Content-Aware Feature Reassembly Network)图像增强网络,以提升图像质量,并优化输入特征;其次,加入通道自适应特征融合(iCAFF)模块,以增强小目标及重叠遮挡目标的提取能力;再次,提出特征融合金字塔结构BiHS-FPN(Bi-directional High-level Screening Feature Pyramid Network),以增强多尺度特征的融合能力;最后,设计IF-MPDIoU(Inner-Focaler-Minimum Point Distance based Intersection over Union)损失函数,以通过调整变量因子,聚焦关键样本,并增强泛化能力。实验结果表明,在自制数据集和SODA10M数据集上,ITD-YOLOv9算法的检测精度分别为83.8%和56.3%,检测帧率分别为64.8 frame/s和57.4 frame/s。与YOLOv9算法相比,ITD-YOLOv9算法的检测精度分别提升了3.9和2.7个百分点。可见,所提算法有效实现了交通路口的多目标检测。展开更多
文摘针对基于You Only Look Once v2算法的目标检测存在精度低及稳健性差的问题,提出一种车辆目标实时检测的You Only Look Once v2优化算法;该算法以You Only Look Once v2算法为基础,通过增加网络深度,增强特征提取能力,同时,通过添加残差模块,解决网络深度增加带来的梯度消失或弥散问题;该方法将网络结构中低层特征与高层特征进行融合,提升对小目标车辆的检测精度。结果表明,通过在KITTI数据集上进行测试,优化后的算法在检测速度不变的情况下,提高了车辆目标检测精度,平均精度达到0.94,同时提升了小目标检测的准确性。
基金National Natural Science Foundation of China(8166070081260679)Ningxia College FirstClass Discipline Construction Project(Chinese Medicine)Funded Project(NXYLXK2017A06)
文摘OBJECTIVE Mu-Xiang-You-Fang(MXYF)is a classic prescription of Hui medicine,composed of five herbs,which has been used to treat ischemic stroke for many years.However,the potential pharmacological mecha⁃nisms of MXYF remain unclear.The present research is to investigate the neuroprotective effect of MXYF and its role in modulating autophagy via AMPK/mTOR signaling pathway in the PC12 oxygen-glucose deprivation and reperfusion(OGD/R)injury model.METHODS MXYF was extracted by supercritical CO2 fluid extraction apparatus.PC12 OGD/R injury model was established by oxygen-glucose deprivation for 2 h and reperfusion for 24 h.The effects of MXYF on the viability and cytotoxicity of PC12 cells were determined through cell counting kit(CCK-8)assay.Colorimetric method was performed to determine the LDH leakage rate.The calcium concentration was determined by chemical fluorescence method and the mitochondrial membrane potential was determined through flow cytometry.Monodansylcadaverine(MDC)staining was conducted to detect autophagosome formation.The expression of LC3,Beclin1,p62,p-AMPK,ULK1,p-mTOR and p-p70s6k proteins were determined by immunofluorescence and Western blotting analyses.RESULTS MXYF(1,2 and 4 mg·L^-1)could significantly increase the cell viability and mitochondrial membrane potential,while decreased the release of lactate dehydrogenase(LDH)and calcium concentration in PC12 cells.Mechanistic studies showed that MXYF reduced the LC3-II/LC3-I ratio and inhibited the expression of beclin1,p-AMPK and ULK1.In comparison,the expres⁃sion of p-mTOR,p-p70s6k and p62 were significantly enhanced.CONCLUSION MXYF inhibits autophagy after OGD/Rinduced PC12 cell injury through AMPK-mTOR pathway,thus MXYF might have therapeutic potential for treating the ischemic stroke.
文摘为了解决施工场景下安全帽佩戴检测时,由于人员密集、遮挡和复杂背景等原因造成的小目标漏检和错检的问题,提出一种基于YOLOv8n的双重注意力机制的跨层多尺度安全帽佩戴检测算法。首先,设计微小目标检测头,以提高模型对小目标的检测能力;其次,在特征提取网络中嵌入双重注意力机制,从而更加关注复杂场景下目标信息的特征捕获;然后,将特征融合网络替换成重参数化泛化特征金字塔网络(RepGFPN)改进后的跨层多尺度特征融合结构S-GFPN(Selective layer Generalized Feature Pyramid Network),以实现小目标特征层信息和其他特征层的多尺度融合,并建立长期的依赖关系,从而抑制背景信息的干扰;最后,采用MPDIOU(Intersection Over Union with Minimum Point Distance)损失函数来解决尺度变化不敏感的问题。在公开数据集GDUT-HWD上的实验结果表明,改进后的模型比YOLOv8n的mAP@0.5提升了3.4个百分点,对蓝色、黄色、白色和红色安全帽的检测精度分别提升了2.0、1.1、4.6和9.1个百分点,在密集、遮挡、小目标、反光和黑暗这5类复杂场景下的可视化检测效果也优于YOLOv8n,为实际施工场景中安全帽佩戴检测提供了一种有效方法。
文摘针对目前遥感图像小目标检测任务中易出现漏检和误检的问题,提出一种SCS-YOLO[SMCA+CSC+SIoU(shape-aware intersection over union loss)-you only look once]的遥感图像小目标检测算法。首先,针对遥感图像中目标小而聚集的问题,构建空间多尺度卷积注意力(spatial multi-scale convolutional attention,SMCA),提升模型对空间和通道信息的特征提取能力;其次,针对深层网络传递时小目标语义信息容易丢失的问题,设计聚合亚像素卷积(concentrated sub-pixel convolution,CSC),采用多尺度聚合特征提取方法,增强了网络对语义信息的提取能力;最后,将SIoU损失函数替代原模型中的CIoU(complete intersection over union loss)损失函数,加快了网络的收敛速度。SCS-YOLO模型在RSOD和NWPU VHR-10数据集上,平均精确率的平均值(mAP)分别达到97%和90.9%,相较于原模型分别提升了2.2%和2.7%,可见该方法在遥感图像小目标检测任务中的有效性。
文摘针对交通路口图像复杂,小目标难测且目标之间易遮挡以及天气和光照变化引发的颜色失真、噪声和模糊等问题,提出一种基于YOLOv9(You Only Look Once version 9)的交通路口图像的多目标检测算法ITD-YOLOv9(Intersection Target Detection-YOLOv9)。首先,设计CoT-CAFRNet(Chain-of-Thought prompted Content-Aware Feature Reassembly Network)图像增强网络,以提升图像质量,并优化输入特征;其次,加入通道自适应特征融合(iCAFF)模块,以增强小目标及重叠遮挡目标的提取能力;再次,提出特征融合金字塔结构BiHS-FPN(Bi-directional High-level Screening Feature Pyramid Network),以增强多尺度特征的融合能力;最后,设计IF-MPDIoU(Inner-Focaler-Minimum Point Distance based Intersection over Union)损失函数,以通过调整变量因子,聚焦关键样本,并增强泛化能力。实验结果表明,在自制数据集和SODA10M数据集上,ITD-YOLOv9算法的检测精度分别为83.8%和56.3%,检测帧率分别为64.8 frame/s和57.4 frame/s。与YOLOv9算法相比,ITD-YOLOv9算法的检测精度分别提升了3.9和2.7个百分点。可见,所提算法有效实现了交通路口的多目标检测。
