skip to main content
10.1145/3417473.3417474acmotherconferencesArticle/Chapter ViewAbstractPublication PagesitccConference Proceedingsconference-collections
research-article

Waste Classification using Convolutional Neural Network

Published: 18 September 2020 Publication History

Abstract

Recycling of waste from households and industries, is one of the methods that has been proposed to reduce the ever-increasing pressure on landfills. Different types of waste types warrant different management techniques and hence, proper waste segregation according to its types is essential to facilitate proper recycling. Current existing segregation method still relies on manual hand-picking process. In this paper, a method; based on deep learning and computer vision concepts, to classify wastes using their images into six different waste types (glass, metal, paper, plastic, cardboard and others) has been proposed. Multiple-layered Convolutional Neural Network (CNN) model, specifically the well-known Inception-v3 model has been used for classification of waste, with trained dataset obtained from online sources. High classification accuracy of 92.5% is achievable using the proposed method. It is envisaged that the proposed waste classification method would pave the way for the automation of waste segregation with reduced human involvement and therefore, helps with the waste recycling efforts.

References

[1]
S. Qu et al., "Implications of China's foreign waste ban on the global circular economy," Resour. Conserv. Recycl., vol. 144, pp. 252--255, May 2019.
[2]
E. A. Williams and J. Bentil, "Design and Implementation of a Microcontroller-Based Automatic Waste Management Sorting Unit for a Recycling Plant," Am. J. Eng. Res., vol. 5, no. 07, pp. 248--252, 2016.
[3]
N. K. A. Malik, S. H. Abdullah, and L. A. Manaf, "Community Participation on Solid Waste Segregation Through Recycling Programmes in Putrajaya," Procedia Environ. Sci., vol. 30, pp. 10--14, 2015.
[4]
S. Hulyalkar, R. Deshpande, K. Makode, and S. Kajale, "Implementation of Smartbin using Convolutional Neural Networks," Int. Res. J. Eng. Technol., vol. 05, no. 04, pp. 3353--3358, 2018.
[5]
N. Hayashi, S. Koyanaka, and T. Oki, "Constructing an automatic object-recognition algorithm using labeling @@ R@information for efficient recycling of WEEE," Waste Manag., vol. 88, pp. 337--346, Apr. 2019.
[6]
J. Raihan, P. E. Abas, and C. De Silva Liyanage, "Review of Underwater Image Restoration Algorithms," IET Signal Process., vol. 13, no. 10, pp. 1587--1596, 2019.
[7]
M. Ramashini, P. E. Abas, U. Grafe, and L. C. De Silva, "Bird Sounds Classification Using Linear Discriminant Analysis," in 2019 4th International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), 2019, pp. 1--6.
[8]
R. Andal Virrey, C. De Silva Liyanage, M. Iskandar bin Pg Hj Petra, and P. Emeroylariffion Abas, "Visual data of facial expressions for automatic pain detection," J. Vis. Commun. Image Represent., vol. 61, pp. 209--217, May 2019.
[9]
K. Chauhan and S. Ram, "International Journal of Advance Engineering and Research Image Classification with Deep Learning and Comparison between Different Convolutional Neural Network Structures using Tensorflow and Keras," pp. 533--538, 2018.
[10]
C. Series, "Feature Extraction and Image Recognition with Convolutional Neural Networks Feature Extraction and Image Convolutional Neural Networks Recognition with," 2018.
[11]
A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A Survey of the Recent Architectures of Deep @@ R@Convolutional Neural Networks," no. January, 2019.
[12]
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Commun. ACM, vol. 60, no. 6, pp. 84--90, 2017.
[13]
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818--2826.
[14]
M. S. Islam, F. A. Foysal, N. Neehal, E. Karim, and S. A. Hossain, "InceptB: A CNN Based Classification Approach for Recognizing Traditional Bengali Games," Procedia Comput. Sci., vol. 143, pp. 595--602, 2018.
[15]
S.I. Serengil, "Transfer Learning in Keras Using Inception," 2017. [Online]. Available: https://sefiks.com/2017/12/10/transfer-learning-in-keras-using-inception-v3/. [Accessed: 03-Jun-2020].
[16]
S. Bianco, R. Cadene, L. Celona, and P. Napoletano, "Benchmark Analysis of Representative Deep Neural Network Architectures," IEEE Access, vol. 6, pp. 64270--64277, 2018.
[17]
Sik-Ho Tsang, "Review: Inception-v3 --- 1st Runner Up (Image Classification) in ILSVRC 2015." [Online]. Available: https://medium.com/@sh.tsang/review-inception-v3-1st-runner-up-image-classification-in-ilsvrc-2015-17915421f77c. [Accessed: 23-Mar-2020].

Cited By

View all
  • (2025)Augmented Reality-Enhanced Machine Learning for Automated Recycling DetectionProceedings of the 10th International Conference on Advanced Intelligent Systems and Informatics 202410.1007/978-3-031-77299-3_14(144-151)Online publication date: 1-Jan-2025
  • (2024)Challenges for Future Robotic Sorters of Mixed Industrial Waste: A SurveyIEEE Transactions on Automation Science and Engineering10.1109/TASE.2022.322196921:1(1023-1040)Online publication date: Jan-2024
  • (2024)Towards Sustainable Waste Management: Exploring Machine Learning and Deep Learning Solutions for Biodegradable and Non-Biodegradable Waste Identification2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN)10.1109/ICPCSN62568.2024.00098(585-591)Online publication date: 3-May-2024
  • Show More Cited By

Index Terms

  1. Waste Classification using Convolutional Neural Network

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ITCC '20: Proceedings of the 2020 2nd International Conference on Information Technology and Computer Communications
    August 2020
    64 pages
    ISBN:9781450375399
    DOI:10.1145/3417473
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

    In-Cooperation

    • UPM: Universiti Putra Malaysia
    • Wuhan Univ.: Wuhan University, China

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 18 September 2020

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Convolutional Neural Network
    2. Image processing
    3. Inception-v3
    4. Recycling
    5. Waste Segregation

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    ITCC 2020

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)115
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 10 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2025)Augmented Reality-Enhanced Machine Learning for Automated Recycling DetectionProceedings of the 10th International Conference on Advanced Intelligent Systems and Informatics 202410.1007/978-3-031-77299-3_14(144-151)Online publication date: 1-Jan-2025
    • (2024)Challenges for Future Robotic Sorters of Mixed Industrial Waste: A SurveyIEEE Transactions on Automation Science and Engineering10.1109/TASE.2022.322196921:1(1023-1040)Online publication date: Jan-2024
    • (2024)Towards Sustainable Waste Management: Exploring Machine Learning and Deep Learning Solutions for Biodegradable and Non-Biodegradable Waste Identification2024 4th International Conference on Pervasive Computing and Social Networking (ICPCSN)10.1109/ICPCSN62568.2024.00098(585-591)Online publication date: 3-May-2024
    • (2024)Convolutional Neural Network Based Technique for Efficient Waste Classification2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)10.1109/CVMI61877.2024.10782029(1-6)Online publication date: 19-Oct-2024
    • (2024)Enhancing trash classification in smart cities using federated deep learningScientific Reports10.1038/s41598-024-62003-414:1Online publication date: 23-May-2024
    • (2024)Towards open domain-specific recognition using Quad-Channel Self-Attention Reciprocal Point Learning and AutoencoderKnowledge-Based Systems10.1016/j.knosys.2023.111261284(111261)Online publication date: Jan-2024
    • (2024)Intelligent waste classification approach based on improved multi-layered convolutional neural networkMultimedia Tools and Applications10.1007/s11042-024-18939-w83:36(84095-84120)Online publication date: 6-Apr-2024
    • (2024)Plastic and Non-plastic Waste Classification Using Machine Learning TechniquesData Science and Big Data Analytics10.1007/978-981-99-9179-2_2(15-24)Online publication date: 17-Mar-2024
    • (2024)Intelligent System for Classification of Health-Care Waste Materials Using Convolutional Neural NetworkControl and Information Sciences10.1007/978-981-97-5866-1_37(525-539)Online publication date: 29-Oct-2024
    • (2024)Waste Classification Based on Computer Vision Using Deep Learning Models and Public AwarenessInnovations and Advances in Cognitive Systems10.1007/978-3-031-69197-3_3(28-43)Online publication date: 4-Sep-2024
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media