广西师范大学学报（自然科学版） ›› 2019, Vol. 37 ›› Issue (3): 50-59.doi: 10.16088/j.issn.1001-6600.2019.03.006

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多尺度并行融合的轻量级卷积神经网络设计

范瑞,蒋品群*,曾上游,夏海英,廖志贤,李鹏   

  1. 广西师范大学电子工程学院,广西桂林541004
  • 出版日期:2019-07-12 发布日期:2019-07-12
  • 通讯作者: 蒋品群(1970—),男,广西全州人,广西师范大学副教授,博士。E-mail: pqjiang@mailbox.gxnu.edu.cn
  • 基金资助:
    国家自然科学基金(11465004,61762014);桂林市科学研究与技术开发计划项目(20170113-4)

Design of Lightweight Convolution Neural Network Based on Multi-scale Parallel Fusion

FAN Rui, JIANG Pinqun*, ZENG Shangyou, XIA Haiying, LIAO Zhixian, LI Peng   

  1. College of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004,China
  • Online:2019-07-12 Published:2019-07-12

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摘要： 针对传统深度卷积神经网络分类精度不佳,参数量巨大,难以在内存受限的设备上进行部署的问题,本文提出了一种多尺度并行融合的轻量级卷积神经网络架构PL-Net。首先,将上层输出特征图分别送入两种不同尺度的深度可分离卷积层;然后对并行输出特征信息进行交叉融合,并加入残差学习,设计了一种并行轻量型模块PL-Module;同时,为了更好地提取特征信息,利用尺度降维卷积模块SR-Module来替换传统池化层;最后将上述两个模块相互堆叠构建轻量级网络。在CIFAR10、Caltech256和101_food数据集上进行训练与测试,结果表明:与同等规模的传统CNN、MobileNet-V2网络及SqueezeNet网络相比,PL-Net在减少网络参数的同时,提升了网络的分类精度,适合在内存受限的设备上进行部署。

关键词: 卷积神经网络, 深度可分离卷积, 残差学习, 并行卷积

Abstract: Aiming at the problem that the traditional deep convolutional neural network has poor classification accuracy and large amount of parameters, which is difficult to deploy in memory-constrained devices, a multi-scale parallel fusion lightweight convolutional neural network architecture PL-Net is proposed. Firstly, the upper output feature map is sent to two different scales of the depth separable convolution layer, and then the parallel output is cross-fused with the feature information, and with the residual learning, a parallel lightweight module PL-Module is designed. To better extract the feature information, the scale-dimensional reduction convolutional module(SR-Module) is proposed to replace the traditional pooling layer. Finally, the above two modules are stacked on each other to construct a lightweight network. In the experimental phase, training and testing are performed on the CIFAR10,Caltech 256 and 101_food data sets. The results show that compared with the traditional CNN,MobileNet-V2 and Squeezenet networks of the same scale, PL-Net improves the classification accuracy of the network while reducing the amount of network parameters, and is suitable for deployment on memory-constrained devices.

Key words: convolutional neural network, depthwise separable convolutions, residual learning, parallel convolution

中图分类号: 

  • TP183
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