Garbage Image Classifier using Modified ResNet-50

Bagus Dwi Santoso, Nur Nafi'iyah

Abstract


This research proposes a deep learning model pretrained with ResNet-50 to classify 12 types of garbage. The model uses a modified ResNet-50 architecture with the Adamax and Adadelta optimizers and varying learning rates (0.1, 0.01, and 0.001). Six experiments were conducted to determine the most optimal training parameter configuration for the proposed model. Results show that the model performed best with the Adadelta optimizer and a learning rate of 0.1, achieving a validation accuracy of 93.85%. In comparison, the Adamax optimizer with a learning rate of 0.001 yielded a validation accuracy of 93.44%. Despite these results, there is a tendency for misclassification in the metal, plastic, and white-glass classes. Future work should focus on addressing these misclassification issues by expanding the dataset for these problematic classes. This can be achieved either by collecting additional images specific to these classes or by employing advanced data augmentation techniques to enhance the existing dataset and improve the model's accuracy.

Keywords


Garbage classification; Deep learning; ResNet-50; Adadelta; Adamax

Full Text:

Link Download

References


Ahmed, M. I. B., Alotaibi, R. B., Al-Qahtani, R. A., Al-Qahtani, R. S., Al-Hetela, S. S., Al-Matar, K. A., Al-Saqer, N. K., Rahman, A., Saraireh, L., Youldash, M., & Krishnasamy, G. (2023). Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management. 15, no. 14: 11138. https://doi.org/10.3390/su151411138.

Bircanoglu, C., Atay, M., Beser, F., Genc, O., & Kizrak, M. A. (2018). RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks. 2018 Innovations in Intelligent Systems and Applications (INISTA), 1–7. https://doi.org/10.1109/INISTA.2018.8466276

Cao, L., & Xiang, W. (2020). Application of Convolutional Neural Network Based on Transfer Learning for Garbage Classification. 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), 1032–1036. https://doi.org/10.1109/ITOEC49072.2020.9141699

Chen, H., Xiong, F., Wu, D., Zheng, L., Peng, A., Hong, X., Tang, B., Lu, H., Shi, H., & Zheng, H. (2017). Assessing impacts of data volume and data set balance in using deep learning approach to human activity recognition. 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1160–1165. https://doi.org/10.1109/BIBM.2017.8217821

Dubey, S., Singh, P., Yadav, P., & Singh, K. K. (2020). Household Waste Management System Using IoT and Machine Learning. Procedia Computer Science, 167, 1950–1959. https://doi.org/10.1016/j.procs.2020.03.222

Gupta, T., Joshi, R., Mukhopadhyay, D., Sachdeva, K., Jain, N., Virmani, D., & Garcia-Hernandez, L. (2022). A deep learning approach based hardware solution to categorise garbage in environment. Complex & Intelligent Systems, 8(2), 1129–1152. https://doi.org/10.1007/s40747-021-00529-0

Hussain, M., Bird, J. J., & Faria, D. R. (2019). A Study on CNN Transfer Learning for Image Classification. In A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen, & M. McGinnity (Eds.), Advances in Computational Intelligence Systems (Vol. 840, pp. 191–202). Springer International Publishing. https://doi.org/10.1007/978-3-319-97982-3_16

Irmawartini, I., Mulyati, S. S., & Pujiono, P. (2023). Pengelolaan Sampah dari Hulu ke Hilir di Kota Bandung. Jurnal Kesehatan Lingkungan Indonesia, 22(2), 229–236. https://doi.org/10.14710/jkli.22.2.229-236

Islam, W., Jones, M., Faiz, R., Sadeghipour, N., Qiu, Y., & Zheng, B. (2022). Improving Performance of Breast Lesion Classification Using a ResNet50 Model Optimized with a Novel Attention Mechanism. Tomography, 8(5), 2411–2425. https://doi.org/10.3390/tomography8050200

Jabir, B., & Falih, N. (2021). Dropout, a basic and effective regularization method for a deep learning model: A case study. Indonesian Journal of Electrical Engineering and Computer Science, 24(2), 1009. https://doi.org/10.11591/ijeecs.v24.i2.pp1009-1016

Kocaman, V., Shir, O. M., & Back, T. (2021). Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study. 2020 25th International Conference on Pattern Recognition (ICPR), 10404–10411. https://doi.org/10.1109/ICPR48806.2021.9412907

Koklu, M., & Ozkan, I. A. (2020). Multiclass classification of dry beans using computer vision and machine learning techniques. Computers and Electronics in Agriculture, 174, 105507. https://doi.org/10.1016/j.compag.2020.105507

Maalouf, A., & Mavropoulos, A. (2023). Re-assessing global municipal solid waste generation. Waste Management & Research: The Journal for a Sustainable Circular Economy, 41(4), 936–947. https://doi.org/10.1177/0734242X221074116

Meng, S., & Chu, W.-T. (2020). A Study of Garbage Classification with Convolutional Neural Networks. 2020 Indo – Taiwan 2nd International Conference on Computing, Analytics and Networks (Indo-Taiwan ICAN), 152–157. https://doi.org/10.1109/Indo-TaiwanICAN48429.2020.9181311

Mohamed, M. (2021). Garbage Classification (12 classes) [dataset]. https://www.kaggle.com/datasets/mostafaabla/garbage-classification.

Nguyen, Q. H., Ly, H.-B., Ho, L. S., Al-Ansari, N., Le, H. V., Tran, V. Q., Prakash, I., & Pham, B. T. (2021). Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil. Mathematical Problems in Engineering, 2021, 1–15. https://doi.org/10.1155/2021/4832864

Qonitan, F. D., Wayan Koko Suryawan, I., & Rahman, A. (2021). Overview of Municipal Solid Waste Generation and Energy Utilization Potential in Major Cities of Indonesia. Journal of Physics: Conference Series, 1858(1), 012064. https://doi.org/10.1088/1742-6596/1858/1/012064

Sami, K. N., Amin, Z. M. A., & Hassan, R. (2020). Waste Management Using Machine Learning and Deep Learning Algorithms. International Journal on Perceptive and Cognitive Computing, 6(2), 97–106. https://doi.org/10.31436/ijpcc.v6i2.165

Sarwinda, D., Paradisa, R. H., Bustamam, A., & Anggia, P. (2021). Deep Learning in Image Classification using Residual Network (ResNet) Variants for Detection of Colorectal Cancer. Procedia Computer Science, 179, 423–431. https://doi.org/10.1016/j.procs.2021.01.025

Steiniger, Y., Stoppe, J., Meisen, T., & Kraus, D. (2020). Dealing With Highly Unbalanced Sidescan Sonar Image Datasets for Deep Learning Classification Tasks. Global Oceans 2020: Singapore – U.S. Gulf Coast, 1–7. https://doi.org/10.1109/IEEECONF38699.2020.9389373

Victor Ikechukwu, A., Murali, S., Deepu, R., & Shivamurthy, R. C. (2021). ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images. Global Transitions Proceedings, 2(2), 375–381. https://doi.org/10.1016/j.gltp.2021.08.027

Wang, C., Qin, J., Qu, C., Ran, X., Liu, C., & Chen, B. (2021). A smart municipal waste management system based on deep-learning and Internet of Things. Waste Management, 135, 20–29. https://doi.org/10.1016/j.wasman.2021.08.028

Xia, W., Jiang, Y., Chen, X., & Zhao, R. (2022). Application of machine learning algorithms in municipal solid waste management: A mini review. Waste Management & Research: The Journal for a Sustainable Circular Economy, 40(6), 609–624. https://doi.org/10.1177/0734242X211033716

Yang, Z., Xia, Z., Yang, G., & Lv, Y. (2022). A Garbage Classification Method Based on a Small Convolution Neural Network. Sustainability, 14(22), 14735. https://doi.org/10.3390/su142214735




DOI: http://dx.doi.org/10.35671/telematika.v17i2.2873

Refbacks

  • There are currently no refbacks.


 


Indexed by:

 

Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License .