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
【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘...【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘感知增强的视觉SLAM方法。首先,构建了边缘感知约束的低光图像增强模块。通过自适应尺度的梯度域引导滤波器优化Retinex算法,以获得纹理清晰光照均匀的图像,从而显著提升了在低光照和不均匀光照条件下特征提取性能。其次,在视觉里程计中构建了边缘感知增强的特征提取和匹配模块,通过点线特征融合策略有效增强了弱纹理和结构化场景中特征的可检测性和匹配准确性。具体使用边缘绘制线特征提取算法(edge drawing lines,EDLines)提取线特征,定向FAST和旋转BRIEF点特征提取算法(oriented fast and rotated brief,ORB)提取点特征,并利用基于网格运动统计(grid-based motion statistics,GMS)和比值测试匹配算法进行精确匹配。最后,将该方法与ORB-SLAM2、ORB-SLAM3在TUM数据集和煤矿井下实景数据集上进行了全面实验验证,涵盖图像增强、特征匹配和定位等多个环节。【结果和结论】结果表明:(1)在TUM数据集上的测试结果显示,所提方法与ORB-SLAM2相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了4%~38.46%、8.62%~50%;与ORB-SLAM3相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了0~61.68%、3.63%~47.05%。(2)在煤矿井下实景实验中,所提方法的定位轨迹更接近于相机运动参考轨迹。(3)有效提高了视觉SLAM在煤矿井下特征退化场景中的准确性和鲁棒性,为视觉SLAM技术在煤矿井下的应用提供了技术解决方案。研究面向井下特征退化场景的视觉SLAM方法,对于推动煤矿井下移动式装备机器人化具有重要意义。展开更多
电缆隧道封闭狭长,存在重复布设的电缆架和相似的场景纹理,属于退化场景.针对该场景,提出了1种基于点线特征融合的视觉惯导SLAM(simultaneous localization and mapping)算法.该算法通过长度抑制和短线拟合来改进高维线特征,使其能够更...电缆隧道封闭狭长,存在重复布设的电缆架和相似的场景纹理,属于退化场景.针对该场景,提出了1种基于点线特征融合的视觉惯导SLAM(simultaneous localization and mapping)算法.该算法通过长度抑制和短线拟合来改进高维线特征,使其能够更有效地描述结构化显著的隧道场景.此外,针对电缆隧道中特征相似导致的回环检测失败问题,引入具有高效识别和精确位姿估计的ArUco标记,限定回环发生区域,并利用最小化位姿变换筛选最佳回环帧,从而提升回环检测的准确度和定位精度.最后,在电缆隧道内采集数据集并进行实验验证.结果表明,相对于VINS–Mono(visual inertial system–Mono),所提算法的绝对轨迹精度平均提升了69.73%,满足了电缆隧道巡检的应用需求.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【目的】煤矿井下普遍存在低照度、弱纹理和结构化的特征退化场景,导致视觉SLAM(visual simultaneous localization and mapping)系统面临有效特征不足或误匹配率高的问题,严重制约了其定位的准确性和鲁棒性。【方法】提出一种基于边缘感知增强的视觉SLAM方法。首先,构建了边缘感知约束的低光图像增强模块。通过自适应尺度的梯度域引导滤波器优化Retinex算法,以获得纹理清晰光照均匀的图像,从而显著提升了在低光照和不均匀光照条件下特征提取性能。其次,在视觉里程计中构建了边缘感知增强的特征提取和匹配模块,通过点线特征融合策略有效增强了弱纹理和结构化场景中特征的可检测性和匹配准确性。具体使用边缘绘制线特征提取算法(edge drawing lines,EDLines)提取线特征,定向FAST和旋转BRIEF点特征提取算法(oriented fast and rotated brief,ORB)提取点特征,并利用基于网格运动统计(grid-based motion statistics,GMS)和比值测试匹配算法进行精确匹配。最后,将该方法与ORB-SLAM2、ORB-SLAM3在TUM数据集和煤矿井下实景数据集上进行了全面实验验证,涵盖图像增强、特征匹配和定位等多个环节。【结果和结论】结果表明:(1)在TUM数据集上的测试结果显示,所提方法与ORB-SLAM2相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了4%~38.46%、8.62%~50%;与ORB-SLAM3相比,绝对轨迹误差、相对轨迹误差的均方根误差分别降低了0~61.68%、3.63%~47.05%。(2)在煤矿井下实景实验中,所提方法的定位轨迹更接近于相机运动参考轨迹。(3)有效提高了视觉SLAM在煤矿井下特征退化场景中的准确性和鲁棒性,为视觉SLAM技术在煤矿井下的应用提供了技术解决方案。研究面向井下特征退化场景的视觉SLAM方法,对于推动煤矿井下移动式装备机器人化具有重要意义。
