In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the op...In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.展开更多
为解决由于无人机视角下毛竹林的形状和纹理复杂,现有方法在分割精度和鲁棒性方面表现不佳的问题,提出了一种应用跨领域适应和偏移量引导的毛竹林分割网络——BFSNet。以百山祖国家公园为试验区,利用无人机拍摄周边毛竹林图像构建数据...为解决由于无人机视角下毛竹林的形状和纹理复杂,现有方法在分割精度和鲁棒性方面表现不佳的问题,提出了一种应用跨领域适应和偏移量引导的毛竹林分割网络——BFSNet。以百山祖国家公园为试验区,利用无人机拍摄周边毛竹林图像构建数据集。为增强模型的特征提取能力,提出跨领域适应模块以有效利用源模型的强特征提取能力,并结合自主学习提取适用于毛竹林分割任务的特征,利用两者的优势进行互补。为提高模型对于不同形状毛竹林的识别和定位能力,结合可变形卷积的偏移量引导模块,引入可学习的偏移量参数,以适应不同形状的毛竹林目标。将BFSNet在DeepGlobe Land Cover Classification Challenge和自制数据集上进行模型训练和测试,并与多种主流图像分割方法进行对比。结果表明:BFSNet在交并比、Dice系数、精确率和召回率4项指标上均取得了最优的性能表现,分别获得了76.04%和71.93%的交并比。与多种主流的图像分割模型相比,BFSNet在毛竹林的分割效果方面表现最为出色,对毛竹林形状的精确建模能力能够有效地应对不同形态的毛竹林。展开更多
基金Supported by the National Natural Science Foundation of China(12071133)Natural Science Foundation of Henan Province(252300421993)Key Scientific Research Project of Higher Education Institutions in Henan Province(25B110005)。
文摘In this paper,an adaptive cubic regularisation algorithm based on affine scaling methods(ARCBASM)is proposed for solving nonlinear equality constrained programming with nonnegative constraints on variables.From the optimality conditions of the problem,we introduce appropriate affine matrix and construct an affine scaling ARC subproblem with linearized constraints.Composite step methods and reduced Hessian methods are applied to tackle the linearized constraints.As a result,a standard unconstrained ARC subproblem is deduced and its solution can supply sufficient decrease.The fraction to the boundary rule maintains the strict feasibility(for nonnegative constraints on variables)of every iteration point.Reflection techniques are employed to prevent the iterations from approaching zero too early.Under mild assumptions,global convergence of the algorithm is analysed.Preliminary numerical results are reported.
文摘为解决由于无人机视角下毛竹林的形状和纹理复杂,现有方法在分割精度和鲁棒性方面表现不佳的问题,提出了一种应用跨领域适应和偏移量引导的毛竹林分割网络——BFSNet。以百山祖国家公园为试验区,利用无人机拍摄周边毛竹林图像构建数据集。为增强模型的特征提取能力,提出跨领域适应模块以有效利用源模型的强特征提取能力,并结合自主学习提取适用于毛竹林分割任务的特征,利用两者的优势进行互补。为提高模型对于不同形状毛竹林的识别和定位能力,结合可变形卷积的偏移量引导模块,引入可学习的偏移量参数,以适应不同形状的毛竹林目标。将BFSNet在DeepGlobe Land Cover Classification Challenge和自制数据集上进行模型训练和测试,并与多种主流图像分割方法进行对比。结果表明:BFSNet在交并比、Dice系数、精确率和召回率4项指标上均取得了最优的性能表现,分别获得了76.04%和71.93%的交并比。与多种主流的图像分割模型相比,BFSNet在毛竹林的分割效果方面表现最为出色,对毛竹林形状的精确建模能力能够有效地应对不同形态的毛竹林。
