A Waste Classification Method Based on a Multilayer Hybrid Convolution Neural Network
Source abstract
Abstract
With the rapid development of deep learning technology, a variety of network models for classification have been proposed, which is beneficial to the realization of intelligent waste classification. However, there are still some problems with the existing models in waste classification such as low classification accuracy or long running time. Aimed at solving these problems, in this paper, a waste classification method based on a multilayer hybrid convolution neural network (MLH-CNN) is proposed. The network structure of this method is similar to VggNet but simpler, with fewer parameters and a higher classification accuracy. By changing the number of network modules and channels, the performance of the proposed model is improved. Finally, this paper finds the appropriate parameters for waste image classification and chooses the optimal model as the final model. The experimental results show that, compared with some recent works, the proposed method has a simpler network structure and higher waste classification accuracy. A large number of experiments in a TrashNet dataset show that the proposed method achieves a classification accuracy of up to 92.6%, which is 4.18% and 4.6% higher than that of some state-of-the-art methods, and proves the effectiveness of the proposed method.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Municipal Solid Waste Management · Brain Tumor Detection and Classification
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China
College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116000, China
Funding
National Natural Science Foundation of China · Heilongjiang Science Foundation Project of China · Fundamental Research Funds in Heilongjiang Provincial Universities of China
References (42)
Show source-provided references
1. Intelligent Liberation and classification of electronic scrap10.1016/S0032-5910(99)00151-5 ↗
2. Lui, C., Sharan, L., Adelson, E.H., and Rosenholtz, R. (2010, January 13–18). Exploring features in a bayesian framework for material recognition. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.
3. Deep learning based robot for automatically picking up garbage on the grass10.1109/TCE.2018.2859629 ↗
4. Lin, I., Loyola-González, O., Monroy, R., and Medina-Pérez, M.A. (2021). A review of fuzzy and pattern-based approaches for class imbalance problems. Appl. Sci., 11.10.3390/app11146310 ↗
5. Research and modeling of the process of sulfur production in the claus reactor using the method of artificial neural networks
6. Fuzzy spectral and spatial feature integration for classification of nonferrous materials in hyperspectral data10.1109/TII.2009.2031238 ↗
7. Millimeter-wave imaging for recycled paper classification10.1109/JSEN.2015.2512106 ↗
8. An automated machine learning approach for smart waste management systems10.1109/TII.2019.2915572 ↗
9. Zheng, J., Xu, M., Cai, M., Wang, Z., and Yang, M. (2019). Modeling group behavior to study innovation diffusion based on cognition and network: An analysis for garbage classification system in Shanghai, China. Int. J. Environ. Res. Public Health, 16.10.3390/ijerph16183349 ↗
10. Chu, Y., Huang, C., Xie, X., Tan, B., Kamal, S., and Xiong, X. (2018). Multilayer hybrid deep-learning method for waste classification and recycling. Comput. Intell. Neurosci., 1–9.10.1155/2018/5060857 ↗
11. Donovan, J. (2018). Auto-Trash Sorts Waste Automatically at the TechCrunch Disrupt Hackathon, Techcrunch Disrupt Hackaton.
12. Using ANN model to determine future waste characteristics in order to achieve specific waste management targets-case study of Serbia
13. Imagenet classification with deep convolutional neural networks
14. Classification of nonferrous metals using magnetic induction spectroscopy10.1109/TII.2017.2786778 ↗
15. Research on waste classification and recognition based on hyperspectral imaging technology
16. Classification of different types of metal from recyclable household waste for automatic waste separation system10.11591/eei.v8i2.1488 ↗
17. Multi-scale CNN based garbage detection of airborne hyperspectral data10.1109/ACCESS.2019.2932117 ↗
18. Identification of black plastics based on fuzzy rbf neural networks: Focused on data preprocessing techniques through fourier transform infrared radiation10.1109/TII.2017.2771254 ↗
19. Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv.
20. Kennedy, T. (2018). OscarNet: Using transfer learning to classify disposable waste. CS230 Report: Deep Learning, Stanford University.
21. residual networks behave like ensembles of relatively shallow networks
22. Comparative efficiency of algorithms based on support vector machines for binary classification10.1134/S0006350915010145 ↗
23. Intelligent waste classification system using deep learning convolutional neural network10.1016/j.promfg.2019.05.086 ↗
24. Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., and Yan, J. (2019, January 15–20). SiamRPN++: Evolution of siamese visual tracking with very deep networks. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00441 ↗
25. Zhihong, C., Hebin, Z., Yanbo, W., Binyan, L., and Yu, L. (2017, January 26–28). A vision-based robotic grasping system using deep learning for garbage sorting2017. Proceedings of the 36th Chinese Control Conference (CCC), Dalian, China.10.23919/ChiCC.2017.8029147 ↗
26. Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv.
27. Rabano, S.L., Cabatuan, M.K., Sybingco, E., Dadios, E.P., and Calilung, E.J. (December, January 29). Common garbage classification using mobilenet. Proceedings of the 2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Baguio City, Philippines.10.1109/HNICEM.2018.8666300 ↗
28. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
29. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., and Rabinovich, A. (2015, January 7–12). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2015.7298594 ↗
30. Automatic image-based waste classification
31. K-Nearest neighbors on road networks: A journey in experimentation and in-memory implementation10.14778/2904121.2904125 ↗
32. Costa, B.S., Bernardes, A.C., Pereira, J.V., Zampa, V.H., Pereira, V.A., Matos, G.F., and Silva, A.F. (2018). Artificial intelligence in automated sorting in trash recycling. Anais do XV Encontro Nacional de Inteligência Artificial e Computacional, SBC.10.5753/eniac.2018.4416 ↗
33. Miao, N., Song, Y., Zhou, H., and Li, L. (2020, January 13). Do you have the right scissors? Tailoring pre-trained language models via monte-carlo methods. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Washington, DC, USA.10.18653/v1/2020.acl-main.314 ↗
34. Ioffe, S., and Szegedy, C. (2015, January 6–11). Batch Normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France.
35. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv.
36. A stochastic approximation method10.1214/aoms/1177729586 ↗
37. Yang, M., and Thung, G. (2016). Classification of trash for recyclability status. CS229 Project Report, Publisher Name.
38. Ren, S., He, K., Girshick, R., and Sun, J. (2016, January 27–30). Faster R-CNN: Towards real-time object detection with region proposal networks. Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence, Las Vegas, NV, USA.
39. Awe, O., Mengistu, R., and Sreedhar, V. (2017). Smart trash net: Waste localization and classification. arXiv, preprint.
40. Satvilkar, M. (2018). Image Based Trash Classification using Machine Learning Algorithms for Recyclability Status. [Master’s Thesis, National College of Ireland].
41. Melinte, D.O., Dumitriu, D., Mărgăritescu, M., and Ancuţa, P.N. (2020). Deep Learning Computer Vision for Sorting and Size Determination of Municipal Waste, Springer.10.1007/978-3-030-26991-3_14 ↗
42. Endah, S.N., and Shiddiq, I.N. (2020, January 10–11). Xception architecture transfer learning for waste classification. Proceedings of the 2020 4th International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia.