APPLICATION RESEARCH OF LIGHTWEIGHT NETWORK LW-GCNet IN GARBAGE CLASSIFICATION

XIA Jingming; XU Zifeng; TAN Lin

doi:10.13205/j.hjgc.202302023

Volume 41 Issue 2

Feb. 2023

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Article Navigation > ENVIRONMENTAL ENGINEERING > 2023 > 41(2): 173-180

XIA Jingming, XU Zifeng, TAN Lin. APPLICATION RESEARCH OF LIGHTWEIGHT NETWORK LW-GCNet IN GARBAGE CLASSIFICATION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 173-180. doi: 10.13205/j.hjgc.202302023

Citation:

XIA Jingming, XU Zifeng, TAN Lin. APPLICATION RESEARCH OF LIGHTWEIGHT NETWORK LW-GCNet IN GARBAGE CLASSIFICATION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 173-180. doi: 10.13205/j.hjgc.202302023

XIA Jingming, XU Zifeng, TAN Lin. APPLICATION RESEARCH OF LIGHTWEIGHT NETWORK LW-GCNet IN GARBAGE CLASSIFICATION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 173-180. doi: 10.13205/j.hjgc.202302023

Citation:

XIA Jingming, XU Zifeng, TAN Lin. APPLICATION RESEARCH OF LIGHTWEIGHT NETWORK LW-GCNet IN GARBAGE CLASSIFICATION[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(2): 173-180. doi: 10.13205/j.hjgc.202302023

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APPLICATION RESEARCH OF LIGHTWEIGHT NETWORK LW-GCNet IN GARBAGE CLASSIFICATION

doi: 10.13205/j.hjgc.202302023

1. School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. Engineering Research Center of Digital Forensics Ministry of Education, Nanjing University of Information Science & Technology, Nanjing 210044, China

Received Date: 2022-05-02
Available Online: 2023-05-25
Publish Date: 2023-02-01

Abstract

Abstract

Garbage classification is an important way to build a green city. The traditional garbage classification is commonly carried out manually, the classification is not thorough, and the labor intensity classification is high, which is not conducive to environmental protection and resource reuse. In order to improve the accuracy of garbage classification, this paper proposed a lightweight network model LW-GCNet (light weight garbage classify network) based on VGG16. The network model performed feature extraction by introducing depthwise separable convolution and SE (squeeze-and-excitation) modules, and organically fused the shallow and deep features of junk images. These modules enhanced the dependencies between channels of garbage images to be classified while reducing the computational complexity of the model and providing multi-level semantic information for accurate classification. In addition, the LW-GCNet model adopted adaptive max pooling and global average pooling to replace the fully connected layer in the VGG16 network, which effectively reduced the number of parameters. The performance of LW-GCNet was validated using the dataset GRAB125 consisting of four types of garbage images. The experimental results showed that, on the premise of ensuring the recognition speed, the average recognition accuracy rate of this method reached 77.17%, and the parameter quantity was 3.15 M, making it easy to be deployed in outdoor embedded systems.
- garbage classification,
- lightweight network,
- squeeze-and-excitation module,
- feature fusion

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References

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