首页> 外国专利> FILTER DESIGN FOR SMALL TARGET DETECTION ON INFRARED IMAGERY USING NORMALIZED-CROSS-CORRELATION LAYER IN NEURAL NETWORKS

FILTER DESIGN FOR SMALL TARGET DETECTION ON INFRARED IMAGERY USING NORMALIZED-CROSS-CORRELATION LAYER IN NEURAL NETWORKS

机译:基于神经网络中的归一化交叉相关层的红外图像小目标检测的过滤器设计

摘要

A filter design method for a small target detection on infrared imagery using a normalized-cross-correlation layer in neural networks, including the steps of: Normalizing inputs and filters of a convolutional neural network, wherein normalizing inputs and filters of the convolutional neural network provides faster convergence in a limited database. Defining a forward function of a normalization layer in the convolutional neural network, wherein the forward function of the normalization layer in the convolutional neural network is used for training a neural network. Defining a derivative function of the normalization layer for a back propagation in a neural network training phase. Training created neural networks with datasets, wherein the datasets consist of target and background views and using trained neural networks in the small target detection.
机译:一种过滤器设计方法,用于在神经网络中使用归一化交叉相关层对红外图像的小目标检测,包括步骤:卷积神经网络的标准化输入和滤波器,其中卷积神经网络的标准化输入和过滤器提供有限数据库中的更快收敛。定义卷积神经网络中的归一化层的前向功能,其中卷积神经网络中的归一化层的前向功能用于训练神经网络。定义归一化层的衍生功能,以在神经网络训练阶段中的反向传播。训练创建了利用数据集的神经网络,其中数据集包括目标和背景视图,并在小目标检测中使用训练的神经网络。

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