| 基于改进双流卷积递归神经网络的RGB-D物体识别方法 |
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| 引用本文： | 李珣,李林鹏,Alexander Lazovik,王文杰,王晓华.基于改进双流卷积递归神经网络的RGB-D物体识别方法[J].光电工程,2021,48(2):21-30. |
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| 作者姓名： | 李珣 李林鹏 Alexander Lazovik 王文杰 王晓华 |
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| 作者单位： | 西安工程大学电子信息学院,陕西西安 710048;格罗宁根大学伯努利实验室,格罗宁根 9747 AG,荷兰;西安工程大学电子信息学院,陕西西安 710048;格罗宁根大学伯努利实验室,格罗宁根 9747 AG,荷兰 |
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| 基金项目： | 国家自然科学基金资助项目(61971339);陕西省自然科学基础研究计划项目(2019JM567);中国纺织工业联合会科技指导性项目(2018094);大学生创新创业训练计划项目(201910709019)。 |
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| 摘 要： | 为了提高基于图像的物体识别准确率,提出一种改进双流卷积递归神经网络的RGB-D物体识别算法(Re-CRNN).将RGB图像与深度光学信息结合,基于残差学习对双流卷积神经网络(CNN)进行改进:增加顶层特征融合单元,在RGB图像和深度图像中学习联合特征,将提取的RGB和深度图像的高层次特征进行跨通道信息融合,继而使用So...
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| 关 键 词： | RGB-D图像 结构光 物体识别 深度学习 深度图像 |
RGB-D object recognition algorithm based on improved double stream convolution recursive neural network |
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| Li Xun,Li Linpeng,Alexander Lazovik,Wang Wenjie,Wang Xiaohua.RGB-D object recognition algorithm based on improved double stream convolution recursive neural network[J].Opto-Electronic Engineering,2021,48(2):21-30. |
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| Authors: | Li Xun Li Linpeng Alexander Lazovik Wang Wenjie Wang Xiaohua |
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| Affiliation: | (School of Electronics and Information,Xi’an Polytechnic University,Xi’an,Shaanxi 710048,China;Bernoulli Institute,University of Groningen,Groningen 9747 AG,Netherlands) |
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| Abstract: | An algorithm(Re-CRNN)of image processing is proposed using RGB-D object recognition,which is improved based on a double stream convolutional recursive neural network,in order to improve the accuracy of object recognition.Re-CRNN combines RGB image with depth optical information,the double stream convolutional neural network(CNN)is improved based on the idea of residual learning as follows:top-level feature fusion unit is added into the network,the representation of federation feature is learning in RGB images and depth images and the high-level features are integrated in across channels of the extracted RGB images and depth images information,after that,the probability distribution was generated by Softmax.Finally,the experiment was carried out on the standard RGB-D data set.The experimental results show that the accuracy was 94.1%using Re-CRNN algorithm for the RGB-D object recognition,which was significantly improved compared with the existing image-based object recognition methods. |
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| Keywords: | RGB-D image structured light object recognition deep learning depth image |
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