GARBAGE IMAGE CLASSIFICATION BY LIGHTWEIGHT RESIDUAL NETWORK

YUAN Jian-ye; NAN Xin-yuan; CAI Xin; LI Cheng-rong

doi:10.13205/j.hjgc.202102017

Volume 39 Issue 2

Jul. 2021

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2021 > 39(2): 110-115

YUAN Jian-ye, NAN Xin-yuan, CAI Xin, LI Cheng-rong. GARBAGE IMAGE CLASSIFICATION BY LIGHTWEIGHT RESIDUAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(2): 110-115. doi: 10.13205/j.hjgc.202102017

Citation:

YUAN Jian-ye, NAN Xin-yuan, CAI Xin, LI Cheng-rong. GARBAGE IMAGE CLASSIFICATION BY LIGHTWEIGHT RESIDUAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(2): 110-115. doi: 10.13205/j.hjgc.202102017

YUAN Jian-ye, NAN Xin-yuan, CAI Xin, LI Cheng-rong. GARBAGE IMAGE CLASSIFICATION BY LIGHTWEIGHT RESIDUAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(2): 110-115. doi: 10.13205/j.hjgc.202102017

Citation:

YUAN Jian-ye, NAN Xin-yuan, CAI Xin, LI Cheng-rong. GARBAGE IMAGE CLASSIFICATION BY LIGHTWEIGHT RESIDUAL NETWORK[J]. ENVIRONMENTAL ENGINEERING , 2021, 39(2): 110-115. doi: 10.13205/j.hjgc.202102017

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GARBAGE IMAGE CLASSIFICATION BY LIGHTWEIGHT RESIDUAL NETWORK

doi: 10.13205/j.hjgc.202102017

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

Received Date: 2020-04-06
Available Online: 2021-07-19

Abstract

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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References(21)

References

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