Human dignity is widely regarded as the foundation of modern human rights concepts and norms.The doctrine of human dignity in Chinese culture enjoys a long and profound history,and the pre-Qin assertion that“humans a...Human dignity is widely regarded as the foundation of modern human rights concepts and norms.The doctrine of human dignity in Chinese culture enjoys a long and profound history,and the pre-Qin assertion that“humans are the most precious”is the most representative expression of human dignity.Ancient Chinese scholars’elaboration on dignity was ethically oriented;they affirmed that humans have the freedom to make moral choices in spirit and required them to assume moral responsibilities towards others and society.Since modern times,with the changes of the times and the introduction of Western liberalism,the traditional view of moral dignity has seen a significant expansion of its scope,incorporating freedom in economic,political,and social life into the category of human dignity and establishing a closer connection with human rights.In contemporary China,under the guidance of Marxism,the view of dignity regards the free,comprehensive,and common development of human beings as the intrinsic requirement and external manifestation of human dignity,takes the rights to subsistence and development as the primary and fundamental human rights,and comprehensively safeguards the dignity of every individual through the coordinated protection of economic,political,social,and cultural rights.展开更多
森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多...森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多视角可见光图像森林火灾高点检测数据集FFHPV(forest fire of high point view),旨在增强模型对多视角火点知识的学习能力;其次,引入全维动态卷积,构建空间金字塔池化层(OD-SPP),以此提升模型针对多视角数据的火点特征提取能力;最后,引入具有动态非单调聚焦机制的边界框定位损失函数Wise-IoU(wise intersection over union),降低低质量数据对模型精度的影响,提高小目标火点的检测能力.实验结果表明:所提出的FFD-YOLO方法相较于YOLOv7,精度提高3.9%,召回率提高3.7%,均值平均精度提高4.0%,F1分数提高0.038;同时,在与YOLOv5、YOLOv8、DDQ(dense distinct query)、DINO(detection transformer with improved denoising anchor boxes)、Faster R-CNN、Sparse R-CNN、Mask R-CNN、FCOS和YOLOX的对比实验中,FFD-YOLO具有最高的精度75.3%、召回率73.8%、均值平均精度77.6%和F1分数0.745,验证了该方法的可行性与有效性.展开更多
基金“Research on the Content and Realization Methods of Citizens’Participation Rights,”a major project(Project Number 21JJD820003)funded by the National Human Rights Education and Training Base of the Ministry of Education of China.
文摘Human dignity is widely regarded as the foundation of modern human rights concepts and norms.The doctrine of human dignity in Chinese culture enjoys a long and profound history,and the pre-Qin assertion that“humans are the most precious”is the most representative expression of human dignity.Ancient Chinese scholars’elaboration on dignity was ethically oriented;they affirmed that humans have the freedom to make moral choices in spirit and required them to assume moral responsibilities towards others and society.Since modern times,with the changes of the times and the introduction of Western liberalism,the traditional view of moral dignity has seen a significant expansion of its scope,incorporating freedom in economic,political,and social life into the category of human dignity and establishing a closer connection with human rights.In contemporary China,under the guidance of Marxism,the view of dignity regards the free,comprehensive,and common development of human beings as the intrinsic requirement and external manifestation of human dignity,takes the rights to subsistence and development as the primary and fundamental human rights,and comprehensively safeguards the dignity of every individual through the coordinated protection of economic,political,social,and cultural rights.
文摘森林火点检测在林火应急救援中起着至关重要的作用.鉴于现有模型在样本质量、多尺度检测以及多视角图像泛化能力方面存在不足,以YOLOv7为基础,提出一种森林火点目标检测方法FFD-YOLO(forest fire detection based on YOLO).首先,构建多视角可见光图像森林火灾高点检测数据集FFHPV(forest fire of high point view),旨在增强模型对多视角火点知识的学习能力;其次,引入全维动态卷积,构建空间金字塔池化层(OD-SPP),以此提升模型针对多视角数据的火点特征提取能力;最后,引入具有动态非单调聚焦机制的边界框定位损失函数Wise-IoU(wise intersection over union),降低低质量数据对模型精度的影响,提高小目标火点的检测能力.实验结果表明:所提出的FFD-YOLO方法相较于YOLOv7,精度提高3.9%,召回率提高3.7%,均值平均精度提高4.0%,F1分数提高0.038;同时,在与YOLOv5、YOLOv8、DDQ(dense distinct query)、DINO(detection transformer with improved denoising anchor boxes)、Faster R-CNN、Sparse R-CNN、Mask R-CNN、FCOS和YOLOX的对比实验中,FFD-YOLO具有最高的精度75.3%、召回率73.8%、均值平均精度77.6%和F1分数0.745,验证了该方法的可行性与有效性.
