计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 166-171.doi: 10.11896/JsJkx.190600179

• 计算机图形学 & 多媒体 • 上一篇    下一篇

多尺度膨胀卷积在图像分类中的应用

吴昊昊, 王方石   

  1. 北京交通大学软件学院 北京 100044
  • 发布日期:2020-07-07
  • 通讯作者: 王方石(fshwang@bJtu.edu.cn)
  • 作者简介:16121734@bJtu.edu.cn

Application of Multi-scale Dilated Convolution in Image Classification

WU Hao-hao and WANG Fang-shi   

  1. School of Software,BeiJing Jiaotong University,BeiJing 100044,China
  • Published:2020-07-07
  • About author:WU Hao-hao, born in 1995, master.His main research interests include deeplearning and so on.
    WANG Fang-shi, born in 1969, Ph.D, professor, is a member of China Computer Federation.Her main research interests include visual information processing and pattern recognition.

摘要: 在采用深度学习进行图像分类时,为减少下采样导致的空间信息损失,往往采用膨胀卷积代替下采样,但尚未有文献研究膨胀卷积作用于不同网络层的性能差异。文中进行了大量图像分类实验,找到了适宜膨胀卷积作用的最佳网络层。 但使用膨胀卷积会丢失近邻点的相关信息,导致网格现象,造成图像部分局部信息的丢失。为消除网格现象,又提出在前述最佳网络层采用多尺度膨胀卷积构建神经网络的方法。实验结果表明,所提出的构建网络方法在图像分类中取得了较好的效果。

关键词: 神经网络, 图像分类, 膨胀卷积, 多尺度

Abstract: In order to reduce the loss of spatial information caused by down sampling,dilated convolution is often used instead of down-sampling in image classification based on deep learning.However,there is no literature on the performance difference of dilated convolution on different network layers.In this paper,a large number of image classification experiments have been carried out,and the best network layer suitable for dilated convolution has been found.However,the use of dilated convolution will lose the information of neighboring points,resulting in grid phenomenon and the loss of partial information of the image.In order to eliminate the grid phenomenon,this paper also proposes a method of constructing neural network by using multi-scale dilated convolution in the optimal network layer mentioned above.The experimental results show that the proposed network construction method achieves good results in image classification.

Key words: Neural network, Image classification, Dilated convolution, Multi-scale

中图分类号: 

  • TP301
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