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Target Recognition Based on Convolutional Neural Network

机译:基于卷积神经网络的目标识别

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One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.
机译:特征目标提取是目标目标识别的重要部分之一,它可以分为特征提取和自动特征提取。传统的神经网络是自动特征提取方法之一,但由于全局连接而导致过度拟合的可能性很高。本文使用的深度学习算法是一种分层自动特征提取方法,经过逐层卷积神经网络(CNN)训练,可以从较低层到较高层提取特征。这些特征具有更大的判别力,有利于物体目标的识别。

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