基于轻量级残差网路的垃圾图片分类方法

袁建野; 南新元; 蔡鑫; 李成荣

doi:10.13205/j.hjgc.202102017

基于轻量级残差网路的垃圾图片分类方法

doi: 10.13205/j.hjgc.202102017

1. 新疆大学电气工程学院, 乌鲁木齐 830047;
2. 中国科学院自动化研究所智能制造技术与系统研究中心, 北京 100190

详细信息

作者简介:
袁建野(1993-),男,硕士研究生,主要研究方向为深度学习,可回收垃圾图像处理。aku_cintapadamu@163.com

通讯作者:
南新元(1967-),男,博士,教授,主要研究方向为机器学习,工业环境技术与应用。nxyxd@sina.com

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- 文章访问数: 281
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- 被引次数: 0
出版历程
- 收稿日期: 2020-04-06
- 网络出版日期: 2021-07-19

GARBAGE IMAGE CLASSIFICATION BY LIGHTWEIGHT RESIDUAL NETWORK

1. College of Electrical Engineering, Xinjiang University, Urumqi 830047, China;
2. Intelligent Manufacturing Technology and System Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

摘要

摘要: 近年来，我国生活垃圾总量每年以10%的速度增长。但是生活垃圾的分类处理能力及技术相对比较有限和落后。基于机器视觉的分类方法一直是广泛使用的方法，而传统的视觉分类网络目前面临着参数多、计算量大、分类精度不高和分类时间长的问题。因此，提出使用最大平均组合池化（Max-AVE Pooling）代替ResNet-50Bottleneck中的最大池化（Max Pooling）与平均池化（AVE Pooling）；通过使用深度可分离卷积代替ResNet-50Bottleneck中的标准卷积的方法对垃圾图片进行分类。实验结果表明，该轻量级残差网路（MaxAVE-Pooling-MobileNet-18，MAPMobileNet-18）与经典分类网络相比能使参数量显著减少10倍，计算量显著减少14倍，又能略微提高精度，非常适合移动手机端、嵌入式设备的实施处理和应用。
- 垃圾分类 /
- 残差网络 /
- 深度可分离卷积 /
- 轻量级网络
Abstract: In recent years, the total domestic garbage in China has been increasing at a rate of 10% per year. However, the technology for the classification and treatment of domestic garbage is relatively limited and backward. The classification method based on machine vision has been widely used. Traditional visual classification net currently faces the problems of sophisticated parameters, large amount of calculation, low classification accuracy, and long classification time. Therefore, this paper proposed to use Max-AVE Pooling instead of Max Pooling or AVE Pooling in ResNet-50Bottleneck, and use the depth separable convolution instead of the standard convolution method in ResNet-50Bottleneck to classify junk images. The experimental results showed that the lightweight residual network (MaxAVE-Pooling-MobileNet-18, MAPMobileNet-18) proposed in this paper could significantly reduce the parameter amount by 10 times and the calculation amount by 14 times, and slightly improve the accuracy compared with the classical classification network. It is very suitable for the implementation and application of mobile phones and embedded devices.
- garbage classification /
- residual network /
- deep separable convolution /
- lightweight net

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参考文献(21)

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