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### 基于多分支瓶颈结构的轻量型图像分类算法研究

1. 陕西师范大学物理学与信息技术学院，陕西 西安 710119
• 收稿日期:2022-08-05 修回日期:2022-11-10 出版日期:2022-11-25
• 作者简介:
• 苏田田 男，1996年出生于甘肃省.2019年在西北师范大学获得学士学位.现为陕西师范大学研究生，主要研究方向为计算机视觉和深度学习. E-mail: sutcarl@hotmail.com
王慧敏 女，1997年出生于陕西省.2019年在陕西师范大学获得学士学位.现为陕西师范大学研究生，主要研究方向为图像分类. E-mail: 1164994333@qq.com
张小凤 （通讯作者） 女，1971年出生于陕西省.现为陕西师范大学物理学与信息技术学院教授，博士生导师.主要研究方向为超声工程、智能信号处理与检测.
• 基金资助:
• 国家自然科学基金 (11874252)

### Research on Lightweight Image Classification Algorithm Based on Multi-branch Bottleneck Structure

SU Tian-tian, WANG Hui-min, ZHANG Xiao-feng

1. School of Physics and Information Technology，Shaanxi Normal University，Xi'an，Shaanxi 710119，China
• Received:2022-08-05 Revised:2022-11-10 Online:2022-11-25
• Supported by:
• National Natural Science Foundation of China (11874252)

Abstract:

The traditional convolutional neural networks have many problems, such as large number of parameters, long training time, and insufficient recognition accuracy of the lightweight models. Based on ResNet network, a lightweight network named RemulbNet(Residual multi-branch structured Network) with multi-branch structure, which increases feature diversity with multi-branch structure in the backbone of the residual structure, reduces the number of model parameters with the depth-separable convolution of variants, and also increases the nonlinear expression capability of the network with Mish activation function. These measures can effectively reduce the model volume and improve the classification accuracy of the network. Using the image recognition database, the network performance is tested. For 5 categories of flower identification, RemulbNet improves the recognition accuracy by 3.9%, reduces the number of model parameters by 71%, reduces the model volume by 77%, and shortens the training time by about 40% compared with the ResNet network. Facing different image classification datasets, RemulbNet also shows excellent performance in terms of recognition accuracy, model volume, training time compared with the lightweight networks(MobileNet v2 and ShuffleNet v2).