Plant phenotype detection plays a crucial role in understanding and studying plant biology,agriculture,and ecology.It involves the quantification and analysis of various physical traits and characteristics of plants,s...Plant phenotype detection plays a crucial role in understanding and studying plant biology,agriculture,and ecology.It involves the quantification and analysis of various physical traits and characteristics of plants,such as plant height,leaf shape,angle,number,and growth trajectory.By accurately detecting and measuring these phenotypic traits,researchers can gain insights into plant growth,development,stress tolerance,and the influence of environmental factors,which has important implications for crop breeding.Among these phenotypic characteristics,the number of leaves and growth trajectory of the plant are most accessible.Nonetheless,obtaining these phenotypes is labor intensive and financially demanding.With the rapid development of computer vision technology and artificial intelligence,using maize field images to fully analyze plant-related information can greatly eliminate repetitive labor and enhance the efficiency of plant breeding.However,it is still difficult to apply deep learning methods in field environments to determine the number and growth trajectory of leaves and stalks due to the complex backgrounds and serious occlusion problems of crops in field environments.To preliminarily explore the application of deep learning technology to the acquisition of the number of leaves and stalks and the tracking of growth trajectories in field agriculture,in this study,we developed a deep learning method called Point-Line Net,which is based on the Mask R-CNN framework,to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks.The experimental results demonstrate that the object detection accuracy(mAP50)of our Point-Line Net can reach 81.5%.Moreover,to describe the position and growth of leaves and stalks,we introduced a new lightweight"key point"detection branch that achieved a magnitude of 33.5 using our custom distance verification index.Overall,these findings provide valuable insights for future field plant phenotype detection,particularly for datasets with dot and line annotations.展开更多
This paper studies representation of rigid combination of a directed line and a reference point on it （here referred to as a ＂point-line＂） using dual quatemions. The geometric problem of rational ruled surface des...This paper studies representation of rigid combination of a directed line and a reference point on it （here referred to as a ＂point-line＂） using dual quatemions. The geometric problem of rational ruled surface design is viewed as the kinematic prob- lem of rational point-line motion design. By using the screw theory in kinematics, mappings from the spaces of lines and point-lines in Euclidean three-dimensional space into the hyperplanes in dual quaternion space are constructed, respectively. The problem of rational point-line motion design is then converted to that of projective Bezier or B-spline image curve design in hyperplane of dual quatemions. This kinematic method can unify the geometric design of ruled surfaces and tool path generation for five-axis numerical control （NC） machining.展开更多
The assembly of hybrid nanomaterials has opened up a new direction for the construction of high-performance anodes for lithium-ion batteries （LIBs）. In this work, we present a straightforward, eco-friendly, one-step...The assembly of hybrid nanomaterials has opened up a new direction for the construction of high-performance anodes for lithium-ion batteries （LIBs）. In this work, we present a straightforward, eco-friendly, one-step hydrothermal protocol for the synthesis of a new type of Fe2OB-SnO2/graphene hybrid, in which zero-dimensional （0D） SnO2 nanoparticles with an average diameter of 8 nm and one-dimensional （1D） Fe203 nanorods with a length of -150 nm are homogeneously attached onto two-dimensional （2D） reduced graphene oxide nanosheets, generating a unique point-line-plane （0D-1D-2D） architecture. The achieved Fe203-SnO2/graphene exhibits a well-defined morphology, a uniform size, and good monodispersity. As anode materials for LIBs, the hybrids exhibit a remarkable reversible capacity of 1,530 mA·g^-1 at a current density of 100 ma·g^-1 after 200 cycles, as well as a high rate capability of 615 mAh·g^-1 at 2,000 mA·g^-1 Detailed characterizations reveal that the superior lithium-storage capacity and good cycle stability of the hybrids arise from their peculiar hybrid nanostructure and conductive graphene matrix, as well as the synergistic interaction among the components.展开更多
目前多数视觉即时定位与地图构建(simultaneous localization and mapping,SLAM)方案都是通过提取环境中的特征点来估计位姿,在纹理较少的弱纹理环境中仍存在较大的局限性。为此,在SLAM系统中引入线特征以保证系统能在弱纹理场景中稳定...目前多数视觉即时定位与地图构建(simultaneous localization and mapping,SLAM)方案都是通过提取环境中的特征点来估计位姿,在纹理较少的弱纹理环境中仍存在较大的局限性。为此,在SLAM系统中引入线特征以保证系统能在弱纹理场景中稳定运行。但目前融合点线的视觉SLAM方案存在实时性和精度不足的问题,因此提出基于改进点线特征融合的的视觉惯性SLAM算法。算法前端中,采用FAST(features from accelerated segment test)角点作为特征点提取算法,对ELSED(enhanced line segment detection)算法进行增加短线合并、梯度阈值参数调整,并将四叉树均匀化分布特征点扩展到点线特征,提出改进的点线特征提取算法,减少高纹理区域和特征分布不均的情况对系统精度的影响。对点线特征的跟踪,均采用改进型光流法追踪,将惯性测量单元(inertial measured unit,IMU)得到的位姿信息和已知的特征点深度计算光流法的初值,代替原本的图像金字塔迭代过程,从而节省计算资源,满足系统的实时性。最后,在实际场景中将该系统与优秀的开源方案进行实验对比,验证了所提算法的实时性和精确性。实验表明,本算法可为工业巡检、仓储物流等场景下的机器人提供高鲁棒性定位解决方案,具有显著的产业应用前景。展开更多
针对弱纹理和变光照环境下基于点特征的视觉SLAM(simultaneous localization and mapping)算法轨迹漂移的问题,提出了一种基于改进自适应阈值ELSED算法(Adaptive-ELSED)的快速点线融合双目视觉SLAM算法。通过在ELSED算法中添加自适应阈...针对弱纹理和变光照环境下基于点特征的视觉SLAM(simultaneous localization and mapping)算法轨迹漂移的问题,提出了一种基于改进自适应阈值ELSED算法(Adaptive-ELSED)的快速点线融合双目视觉SLAM算法。通过在ELSED算法中添加自适应阈值矩阵,动态调整不同光照条件下梯度阈值,并使用长度抑制和短线合并策略,提高线特征的质量。利用基于双目几何约束和图像结构相似性(SSIM)进行快速线段特征三角化。基于历史位姿及误差分析获取初始位姿,通过自适应因子实现光束法平差过程中点线特征的更有效融合。实验结果表明,所提算法在提高线特征质量的同时,耗时仅为LSD算法的50%,线特征匹配速度较传统LBD算法提升67%,挑战性场景下轨迹误差较ORB-SLAM3降低62.2%,系统的平均跟踪帧率为27帧/s,在保证系统实时性的同时,显著提升了系统在弱纹理、变光照环境下的精度和鲁棒性。展开更多
针对基于点线特征的实时定位与建图(simultaneous localization and mapping,SLAM)算法在位姿识别过程中对定位精度的要求,提出一种改进单目视觉惯性同步定位与建图(monocular visual-inertial SLAM with efficient point-line flow fea...针对基于点线特征的实时定位与建图(simultaneous localization and mapping,SLAM)算法在位姿识别过程中对定位精度的要求,提出一种改进单目视觉惯性同步定位与建图(monocular visual-inertial SLAM with efficient point-line flow features,EPLF-VINS)算法。首先,分析了梯度阈值参数对line segment detection by edge drawing(EDLines)线段提取算法的影响;其次,在点特征正向光流追踪后采用逆向光流追踪剔除错误追踪点,提高光流追踪正确率;然后,在EPLF-VINS算法的线段提取处融合一种自适应调节算法,通过计算逆向光流追踪后的点特征光流追踪成功率实时地调节梯度阈值参数,从而实现根据环境的变化动态调整线段提取,更好地平衡计算成本与定位精度的效果;最后,基于Robot Operating System(ROS)平台分析了改进EPLF-VINS算法与对比算法在EuRoc和TUM-VI数据集上的轨迹精度与效率。研究结果表明,改进EPLF-VINS算法绘制的轨迹曲线更加贴合真实轨迹,在保证实时性的同时具有更高的定位精度。展开更多
Inverse Sum Indeg指数(ISI指数)是预测辛烷异构体总表面积的重要拓扑指数。针对ISI指数在图变换下缺乏系统表达式推导的问题,本文给出了具有n个顶点和m条边的简单连通图的细分图、线图、全图、半全点图、半全线图和广义变换图的ISI指...Inverse Sum Indeg指数(ISI指数)是预测辛烷异构体总表面积的重要拓扑指数。针对ISI指数在图变换下缺乏系统表达式推导的问题,本文给出了具有n个顶点和m条边的简单连通图的细分图、线图、全图、半全点图、半全线图和广义变换图的ISI指数的表达式,完善了ISI指数的图变换理论体系,为后续开展多重图变换下的拓扑指数研究奠定了基础。证明过程中,首先根据所研究图的定义确定其顶点和边的度,再对所研究图的边集进行分类并结合ISI指数的定义,建立了所研究图与原图之间的ISI指数关系,最后通过分类讨论,得到了各类图变换下的ISI指数的表达式。本文结果可应用于化学图论与复杂网络科学领域,既能为分子性质预测、分子结构筛选提供量化工具,也能刻画通信、交通等网络的结构演化过程,并为网络拓扑分析与优化设计提供理论依据。展开更多
基金supported by the Project on Genome Refinement of Key Model Organism and its Demonstration and Application-Subtopic 1(2022YFC3400300)the Acquisition and Decoding of Current Signals for Biological Nanopore Sequencing-Subtopic(2019YFA0707003)the Agricultural Science and Technology Innovation Program.
文摘Plant phenotype detection plays a crucial role in understanding and studying plant biology,agriculture,and ecology.It involves the quantification and analysis of various physical traits and characteristics of plants,such as plant height,leaf shape,angle,number,and growth trajectory.By accurately detecting and measuring these phenotypic traits,researchers can gain insights into plant growth,development,stress tolerance,and the influence of environmental factors,which has important implications for crop breeding.Among these phenotypic characteristics,the number of leaves and growth trajectory of the plant are most accessible.Nonetheless,obtaining these phenotypes is labor intensive and financially demanding.With the rapid development of computer vision technology and artificial intelligence,using maize field images to fully analyze plant-related information can greatly eliminate repetitive labor and enhance the efficiency of plant breeding.However,it is still difficult to apply deep learning methods in field environments to determine the number and growth trajectory of leaves and stalks due to the complex backgrounds and serious occlusion problems of crops in field environments.To preliminarily explore the application of deep learning technology to the acquisition of the number of leaves and stalks and the tracking of growth trajectories in field agriculture,in this study,we developed a deep learning method called Point-Line Net,which is based on the Mask R-CNN framework,to automatically recognize maize field RGB images and determine the number and growth trajectory of leaves and stalks.The experimental results demonstrate that the object detection accuracy(mAP50)of our Point-Line Net can reach 81.5%.Moreover,to describe the position and growth of leaves and stalks,we introduced a new lightweight"key point"detection branch that achieved a magnitude of 33.5 using our custom distance verification index.Overall,these findings provide valuable insights for future field plant phenotype detection,particularly for datasets with dot and line annotations.
基金supported by the National Natural Science Foundation of China(Grant Nos.50835004 and 51005087)the National Basic Research Program of China(Grant No.2011CB706804)
文摘This paper studies representation of rigid combination of a directed line and a reference point on it （here referred to as a ＂point-line＂） using dual quatemions. The geometric problem of rational ruled surface design is viewed as the kinematic prob- lem of rational point-line motion design. By using the screw theory in kinematics, mappings from the spaces of lines and point-lines in Euclidean three-dimensional space into the hyperplanes in dual quaternion space are constructed, respectively. The problem of rational point-line motion design is then converted to that of projective Bezier or B-spline image curve design in hyperplane of dual quatemions. This kinematic method can unify the geometric design of ruled surfaces and tool path generation for five-axis numerical control （NC） machining.
基金Acknowledgements The authors gratefully thank the financial support from the National Natural Science Foundation of China （Nos. 11275121, 21471096, and 21371116）, and Program for Innovative Research Team in University （No. IRT13078）.
文摘The assembly of hybrid nanomaterials has opened up a new direction for the construction of high-performance anodes for lithium-ion batteries （LIBs）. In this work, we present a straightforward, eco-friendly, one-step hydrothermal protocol for the synthesis of a new type of Fe2OB-SnO2/graphene hybrid, in which zero-dimensional （0D） SnO2 nanoparticles with an average diameter of 8 nm and one-dimensional （1D） Fe203 nanorods with a length of -150 nm are homogeneously attached onto two-dimensional （2D） reduced graphene oxide nanosheets, generating a unique point-line-plane （0D-1D-2D） architecture. The achieved Fe203-SnO2/graphene exhibits a well-defined morphology, a uniform size, and good monodispersity. As anode materials for LIBs, the hybrids exhibit a remarkable reversible capacity of 1,530 mA·g^-1 at a current density of 100 ma·g^-1 after 200 cycles, as well as a high rate capability of 615 mAh·g^-1 at 2,000 mA·g^-1 Detailed characterizations reveal that the superior lithium-storage capacity and good cycle stability of the hybrids arise from their peculiar hybrid nanostructure and conductive graphene matrix, as well as the synergistic interaction among the components.
文摘目前多数视觉即时定位与地图构建(simultaneous localization and mapping,SLAM)方案都是通过提取环境中的特征点来估计位姿,在纹理较少的弱纹理环境中仍存在较大的局限性。为此,在SLAM系统中引入线特征以保证系统能在弱纹理场景中稳定运行。但目前融合点线的视觉SLAM方案存在实时性和精度不足的问题,因此提出基于改进点线特征融合的的视觉惯性SLAM算法。算法前端中,采用FAST(features from accelerated segment test)角点作为特征点提取算法,对ELSED(enhanced line segment detection)算法进行增加短线合并、梯度阈值参数调整,并将四叉树均匀化分布特征点扩展到点线特征,提出改进的点线特征提取算法,减少高纹理区域和特征分布不均的情况对系统精度的影响。对点线特征的跟踪,均采用改进型光流法追踪,将惯性测量单元(inertial measured unit,IMU)得到的位姿信息和已知的特征点深度计算光流法的初值,代替原本的图像金字塔迭代过程,从而节省计算资源,满足系统的实时性。最后,在实际场景中将该系统与优秀的开源方案进行实验对比,验证了所提算法的实时性和精确性。实验表明,本算法可为工业巡检、仓储物流等场景下的机器人提供高鲁棒性定位解决方案,具有显著的产业应用前景。
文摘针对弱纹理和变光照环境下基于点特征的视觉SLAM(simultaneous localization and mapping)算法轨迹漂移的问题,提出了一种基于改进自适应阈值ELSED算法(Adaptive-ELSED)的快速点线融合双目视觉SLAM算法。通过在ELSED算法中添加自适应阈值矩阵,动态调整不同光照条件下梯度阈值,并使用长度抑制和短线合并策略,提高线特征的质量。利用基于双目几何约束和图像结构相似性(SSIM)进行快速线段特征三角化。基于历史位姿及误差分析获取初始位姿,通过自适应因子实现光束法平差过程中点线特征的更有效融合。实验结果表明,所提算法在提高线特征质量的同时,耗时仅为LSD算法的50%,线特征匹配速度较传统LBD算法提升67%,挑战性场景下轨迹误差较ORB-SLAM3降低62.2%,系统的平均跟踪帧率为27帧/s,在保证系统实时性的同时,显著提升了系统在弱纹理、变光照环境下的精度和鲁棒性。
文摘针对基于点线特征的实时定位与建图(simultaneous localization and mapping,SLAM)算法在位姿识别过程中对定位精度的要求,提出一种改进单目视觉惯性同步定位与建图(monocular visual-inertial SLAM with efficient point-line flow features,EPLF-VINS)算法。首先,分析了梯度阈值参数对line segment detection by edge drawing(EDLines)线段提取算法的影响;其次,在点特征正向光流追踪后采用逆向光流追踪剔除错误追踪点,提高光流追踪正确率;然后,在EPLF-VINS算法的线段提取处融合一种自适应调节算法,通过计算逆向光流追踪后的点特征光流追踪成功率实时地调节梯度阈值参数,从而实现根据环境的变化动态调整线段提取,更好地平衡计算成本与定位精度的效果;最后,基于Robot Operating System(ROS)平台分析了改进EPLF-VINS算法与对比算法在EuRoc和TUM-VI数据集上的轨迹精度与效率。研究结果表明,改进EPLF-VINS算法绘制的轨迹曲线更加贴合真实轨迹,在保证实时性的同时具有更高的定位精度。
文摘Inverse Sum Indeg指数(ISI指数)是预测辛烷异构体总表面积的重要拓扑指数。针对ISI指数在图变换下缺乏系统表达式推导的问题,本文给出了具有n个顶点和m条边的简单连通图的细分图、线图、全图、半全点图、半全线图和广义变换图的ISI指数的表达式,完善了ISI指数的图变换理论体系,为后续开展多重图变换下的拓扑指数研究奠定了基础。证明过程中,首先根据所研究图的定义确定其顶点和边的度,再对所研究图的边集进行分类并结合ISI指数的定义,建立了所研究图与原图之间的ISI指数关系,最后通过分类讨论,得到了各类图变换下的ISI指数的表达式。本文结果可应用于化学图论与复杂网络科学领域,既能为分子性质预测、分子结构筛选提供量化工具,也能刻画通信、交通等网络的结构演化过程,并为网络拓扑分析与优化设计提供理论依据。
