Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 211100100-5.doi: 10.11896/jsjkx.211100100

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Classification Method of Harmful Garbage Images Based on Improved EfficientNetV2

YUAN Hui-lin1, LIU Jun-tao2, HUANG Bi2, HAN Zhen2, FENG Chong2   

  1. 1 College of Management,Northeastern University at Qinhuangdao,Qinhuangdao,Hebei 066004,China
    2 College of Information Science and Engineering,Northeastern University,Shenyang 110819,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:YUAN Hui-lin,born in 1969,Ph.D,professor.Her main research interests include modeling and optimization of complex systems,information retrieval,and artificial intelligence.
    LIU Jun-tao,born in 1996,postgra-duate.His main research interests include machine learning and artificial intelligence.
  • Supported by:
    Northeastern University “Industry University Research Strategic Cooperation Project”(71971050).

PDF (PC)

370

Abstract: With the rapid development of industry,the amount of waste generated has also exploded,making waste disposal a worldwide problem.The Chinese government’s concern for the environment has gradually deepened,and various garbage classification policies and laws and regulations have been continuously introduced to supervise citizens’ garbage classification.Garbage disposal,especially hazardous garbage such as electronic waste,if it is improperly handled,will result in bad influence.Hazardous garbage image data has the characteristics of low data quality and unclear images.The image samples collected from different devices have obvious differences.Therefore,the image processing of hazardous garbage faces huge challenges.At the same time,the classification results of hazardous waste are related to environmental pollution issues.The amount of waste produced is huge,requiring high processing speed and accuracy.This paper proposes a garbage image classification method based on convolutional neural network and hybrid attention mechanism.This method does not need to manually extract features from the input image.Through the deep learning model framework,it overcomes the shortcomings of traditional image processing algorithms,achieves accurate and efficient classification of hazardous waste,and can better identify multiple types of hazardous waste.Experiment shows that the proposed method has an accuracy rate of 97.47% on the harmful-waste data set,the model training time is shorter,and its performance is better than other algorithm models.Using deep learning methods to deploy automated garbage classification models is of great significance to environmental protection.

Key words: Convolutional neural network, Attentional mechanism, Deep learning, Garbage classification, Image classification

CLC Number: 

  • TP391
[1]MA A,MAT B,SF A,et al.Artificial intelligence applications insolid waste management:A systematic research review[J].Waste Management,2020,109:231-246.
[2]YANG M,THUNG G.Classification of trash for recyclability status[J].CS229 Project Report,2016,2016(1):3.
[3]VO A H,LE H S,VO M T,et al.A Novel Framework forTrash Classification Using Deep Transfer Learning[J].IEEE Access,2019,7:178631-178639.
[4]XIE S,GIRSHICK R,DOLLÁR P,et al.Aggregated ResidualTransformations for Deep Neural Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1492-1500.
[5]HOWARD A G,ZHU M,CHEN B,et al.MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications[J].arXiv:1704.04861,2017.
[6]ZHANG X,ZHOU X,LIN M,et al.ShuffleNet:An Extremely Efficient Convolutional Neural Network for Mobile Devices[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6848-6856.
[7]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[J].Advances in Neural Information Processing Systems,2012,25:1097-1105.
[8]GAO Z,XIE J,WANG Q,et al.Global Second-Order PoolingConvolutional Networks[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2020.
[9]WOO S,PARK J,LEE J Y,et al.CBAM:Convolutional Block Attention Module[C]//Proceedings of the European Conference on Computer Vision(ECCV).2018:3-19.
[10]ZHANG Q L,YANG Y B.SA-Net:Shuffle attention for deep convolutional neural networks[C]//2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).IEEE,2021:2235-2239.
[11]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.
[12]TAN M,LE Q V.EfficientNetV2:Smaller Models and FasterTraining[C]//International Conference on Machine Learning.PMLR,2021:10096-10106.
[13]HE K,ZHANG X,REN S,et al.Deep residual learningfor image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[14]HOWARD A,SANDLER M,CHEN B,et al.Searching for MobileNetV3[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV).IEEE,2020.
[15]GAVAI N R,JAKHADE Y A,TRIBHUVAN S A,et al.MobileNets for flower classification using TensorFlow[C]//2017 International Conference on Big Data, IoT and Data Science(BID).IEEE,2017:154-158.
[16]SANDLER M,HOWARD A,ZHU M,et al.MobileNetV2:Inverted Residuals and Linear Bottlenecks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).IEEE,2018.
[17]SUNIL C K,JAIDHAR C D,PATIL N.Cardamom Plant Disease Detection Approach Using EfficientNetV2[J].IEEE Access,2021,10:789-804.
[18]LIU D,WANG W,WU X,et al.EfficientNetv2 Model for Breast Cancer Histopathological Image Classification[C]//2022 3rd International Conference on Electronic Communication and Artificial Intelligence(IWECAI).IEEE,2022:384-387.
[19]KOUMPAROULIS A,POTAMIANOS G.Accurate and Re-source-Efficient Lipreading with Efficientnetv2 and Transfor-mers[C]//2022 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2022:8467-8471.
[20]MA B,WANG X,ZHANG H,et al.CBAM-GAN:generativeadversarial networks based on convolutional block attention module[C]//International Conference on Artificial Intelligence and Security.Cham:Springer,2019:227-236.
[21]QU H,YANG J,SHEN M,et al.Fault diagnosis of rolling bea-ring under time-varying speed conditions based on EfficientNetv2[J]. Measurement Science and Technology,2022,33(6):065023.
[1] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[2] XU Yong-xin, ZHAO Jun-feng, WANG Ya-sha, XIE Bing, YANG Kai. Temporal Knowledge Graph Representation Learning [J]. Computer Science, 2022, 49(9): 162-171.
[3] RAO Zhi-shuang, JIA Zhen, ZHANG Fan, LI Tian-rui. Key-Value Relational Memory Networks for Question Answering over Knowledge Graph [J]. Computer Science, 2022, 49(9): 202-207.
[4] TANG Ling-tao, WANG Di, ZHANG Lu-fei, LIU Sheng-yun. Federated Learning Scheme Based on Secure Multi-party Computation and Differential Privacy [J]. Computer Science, 2022, 49(9): 297-305.
[5] WANG Jian, PENG Yu-qi, ZHAO Yu-fei, YANG Jian. Survey of Social Network Public Opinion Information Extraction Based on Deep Learning [J]. Computer Science, 2022, 49(8): 279-293.
[6] HAO Zhi-rong, CHEN Long, HUANG Jia-cheng. Class Discriminative Universal Adversarial Attack for Text Classification [J]. Computer Science, 2022, 49(8): 323-329.
[7] JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng. Rumor Detection Model Based on Improved Position Embedding [J]. Computer Science, 2022, 49(8): 330-335.
[8] WU Hong-xin, HAN Meng, CHEN Zhi-qiang, ZHANG Xi-long, LI Mu-hang. Survey of Multi-label Classification Based on Supervised and Semi-supervised Learning [J]. Computer Science, 2022, 49(8): 12-25.
[9] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[10] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[11] SUN Qi, JI Gen-lin, ZHANG Jie. Non-local Attention Based Generative Adversarial Network for Video Abnormal Event Detection [J]. Computer Science, 2022, 49(8): 172-177.
[12] HU Yan-yu, ZHAO Long, DONG Xiang-jun. Two-stage Deep Feature Selection Extraction Algorithm for Cancer Classification [J]. Computer Science, 2022, 49(7): 73-78.
[13] DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[14] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[15] LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.