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轻量化网络LW-GCNet在垃圾分类中的应用

夏景明 徐子峰 谈玲

朱佳明, 贺玥澄, 龙定彪, 黄骞, 许文来, 蒲施桦, 简悦. 双模型下基于废弃泡沫砼的HAP结晶法回收养猪废水中磷的影响因素分析[J]. 环境工程, 2023, 41(8): 1-7,17. doi: 10.13205/j.hjgc.202308001
引用本文: 夏景明, 徐子峰, 谈玲. 轻量化网络LW-GCNet在垃圾分类中的应用[J]. 环境工程, 2023, 41(2): 173-180. doi: 10.13205/j.hjgc.202302023
ZHU Jiaming, HE Yuecheng, LONG Dingbiao, HUANG Qian, XU Wenlai, PU Shihua, JIAN Yue. INVESTIGATION OF FACTORS INFLUENCING THE RECOVERY OF PHOSPHORUS FROM SWINE WASTEWATER BY HAP CRYSTALLIZATION BASED ON SPENT FOAM CONCRETE[J]. ENVIRONMENTAL ENGINEERING , 2023, 41(8): 1-7,17. doi: 10.13205/j.hjgc.202308001
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

轻量化网络LW-GCNet在垃圾分类中的应用

doi: 10.13205/j.hjgc.202302023
基金项目: 

国家自然科学基金项目(61871032)

国家重点研发计划(2021YFB2700910)

江苏省高等学校基金项目(20KJB510036)

详细信息
    作者简介:

    夏景明(1980-),男,副教授,主要研究方向为图像分类和目标检测。xiajingming@nuist.edu.cn

    通讯作者:

    谈玲(1979-),女,教授,主要研究方向为大数据和深度学习。001071@nuist.edu.cn

APPLICATION RESEARCH OF LIGHTWEIGHT NETWORK LW-GCNet IN GARBAGE CLASSIFICATION

  • 摘要: 垃圾分类是构建绿色城市的重要途径。传统的垃圾分类是由人工进行,分类不彻底,工作强度大,不利于环境保护与资源再利用。为提高垃圾分类的准确性,提出了一种基于VGG16网络的轻量化网络模型LW-GCNet (light weight garbage classify network)。该网络模型通过引入深度可分离卷积和SE(squeeze-and-excitation)模块来进行特征提取,并将垃圾图像的浅层和深层特征有机融合,在减少计算量的同时,增强了待分类垃圾图像通道之间的依赖关系,为分类提供多层次的语义信息。此外,LW-GCNet模型采用自适应最大池化和全局平均池化取代VGG16网络中的全连接层,有效降低了参数量。利用由4类垃圾图像构成的数据集GRAB125对LW-GCNet性能进行验证。实验结果表明:该方法在保证识别速度的前提下,识别平均准确率达到77.17%,参数量为3.15M,易于在户外的嵌入式系统中进行部署。
  • [1] 孙晓杰, 王春莲, 李倩, 等. 中国生活垃圾分类政策制度的发展演变历程[J]. 环境工程, 2020, 38(8):65-70.
    [2] 任中山, 陈瑛, 王永明. 生活垃圾分类对垃圾焚烧发电产业发展影响的分析[J]. 环境工程, 2021, 39(6):150-153.
    [3] 王肇嘉, 秦玉, 顾军, 等. 生活垃圾焚烧飞灰二噁英控制技术研究进展[J]. 环境工程, 2021,39(10):116-123.
    [4] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25:1097-1105.
    [5] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv:1409.1556, 2014.
    [6] SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-resnet and the impact of residual connections on learning[C]//The thirty-first AAAI conference on artificial intelligence. 2017.
    [7] TAN M X, LE Q V. Efficientnet:rethinking model scaling for convolutional neural networks[C]//the International Conference on Machine Learning. PMLR, 2019:6105-6114.
    [8] YANG M, THUNG G. Classification of trash for recyclability status[R]. CS229 Project Report, 2016, 2016:3.
    [9] ARAL R A, KESKIN R, KAYA M, et al. Classification of trashnet dataset based on deep learning models[C]//2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018:2058-2062.
    [10] RABANO S L, CABATUAN M K, SYBINGCO E, et al. Common garbage classification using mobilenet[C]//2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). IEEE, 2018:1-4.
    [11] KENNEDY T. OscarNet:Using transfer learning to classify disposable waste[R]. CS230 Report:Deep Learning. Stanford University, CA, Winter, 2018.
    [12] OZKAYA U, SEYFI L. Fine-tuning models comparisons on garbage classification for recyclability[J]. arXiv:1908.04393, 2019.
    [13] KANG Z, YANG J, LI G, et al. An automatic garbage classification system based on deep learning[J]. IEEE Access, 2020, 8:140019-140029.
    [14] SHI C P, XIA R Y, WANG L G. A novel multi-branch channel expansion network for garbage image classification[J]. IEEE Access, 2020, 8:154436-154452.
    [15] ZENG M, LU X Z, XU W K, et al. PublicGarbageNet:a deep learning framework for public garbage classification[C]//2020 39th Chinese Control Conference (CCC). IEEE, 2020:7200-7205.
    [16] LI Y F, LIU W. Deep learning-based garbage image recognition algorithm[J]. Applied Nanoscience, 2021:1-10.
    [17] MITTAL G, YAGNIK K B, GARG M, et al. Spotgarbage:smartphone app to detect garbage using deep learning[C]//Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2016:940-945.
    [18] PROENÇA P F, SIMÕES P. TACO:Trash annotations in context for litter detection[J]. arXiv:2003.06975, 2020.
    [19] PANWAR H, GUPTA P K, SIDDIQUI M K, et al. AquaVision:automating the detection of waste in water bodies using deep transfer learning[J]. Case Studies in Chemical and Environmental Engineering, 2020, 2:100026.
    [20] GUO J B, LI Y X, LIN W Y, et al. Network decoupling:from regular to depthwise separable convolutions[J]. arXiv:1808.05517, 2018.
    [21] CHOLLET F. Xception:Deep learning with depthwise separable convolutions[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017:1251-1258.
    [22] ZHAO Q T, SHENG T, WANG Y T, et al. M2det:A single-shot object detector based on multi-level feature pyramid network[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1):9259-9266.
    [23] HAASE D, AMTHOR M. Rethinking depthwise separable convolutions:How intra-kernel correlations lead to improved mobilenets[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020:14600-14609.
    [24] GUO Y H, LI Y D, WANG L Q, et al. Depthwise convolution is all you need for learning multiple visual domains[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1):8368-8375.
    [25] HUA B S, TRAN M K, YEUNG S K. Pointwise convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:984-993.
    [26] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:7132-7141.
    [27] KAR A, RAI N, SIKKA K, et al. Adascan:adaptive scan pooling in deep convolutional neural networks for human action recognition in videos[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017:3376-3385.
    [28] LIN M, CHEN Q, YAN S. Network in network[J]. arXiv:1312.4400, 2013.
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出版历程
  • 收稿日期:  2022-05-02
  • 网络出版日期:  2023-05-25
  • 刊出日期:  2023-02-01

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