A Comparison of MobileNetV2 and VGG16 Architectures with Transfer Learning for Multi-Class Image-Based Waste Classification
DOI:
https://doi.org/10.30871/jaic.v9i4.9958Keywords:
Classification, Convolutional Neural Network, MobileNetV2, VGG16, Waste managementAbstract
Effective waste management represents a global challenge with significant environmental and public health impacts. Despite existing waste classification systems achieving high accuracy rates, a critical research gap exists in determining optimal CNN architectures for real-world deployment constraints, particularly regarding computational efficiency versus classification accuracy trade-offs. We compared two Convolutional Neural Network (CNN) architectures MobileNetV2 and VGG16 for classifying ten types of waste using image-based analysis. Using transfer learning approach, both models were modified for waste classification tasks by adding custom layers to pre-trained models. The dataset contained 19,762 images balanced to 9,440 samples through under-sampling techniques and enhanced with data augmentation to increase variation. Results demonstrated that MobileNetV2 achieved 95.6% test accuracy with precision 0.93, recall 0.93, and F1-score 0.93, significantly outperforming VGG16's 89.13% accuracy with precision 0.91, recall 0.90, and F1-score 0.90. Beyond superior accuracy, MobileNetV2 also demonstrated higher computational efficiency with 350ms/step training time compared to VGG16's 700ms/step, and more consistent performance across all waste categories.
Downloads
References
[1] S. M. Cheema, A. Hannan, and I. M. Pires, “Smart Waste Management and Classification Systems Using Cutting Edge Approach,” Sustainability, vol. 14, no. 16, p. 10226, Aug. 2022, doi: 10.3390/su141610226.
[2] A. U. Gondal et al., “Real Time Multipurpose Smart Waste Classification Model for Efficient Recycling in Smart Cities Using Multilayer Convolutional Neural Network and Perceptron,” Sensors, vol. 21, no. 14, p. 4916, Jul. 2021, doi: 10.3390/s21144916.
[3] Z. Kang, J. Yang, G. Li, and Z. Zhang, “An Automatic Garbage Classification System Based on Deep Learning,” IEEE Access, vol. 8, pp. 140019–140029, 2020, doi: 10.1109/ACCESS.2020.3010496.
[4] M. I. B. Ahmed et al., “Deep Learning Approach to Recyclable Products Classification: Towards Sustainable Waste Management,” Sustainability, vol. 15, no. 14, p. 11138, Jul. 2023, doi: 10.3390/su151411138.
[5] K. Ahmad, K. Khan, and A. Al-Fuqaha, “Intelligent Fusion of Deep Features for Improved Waste Classification,” IEEE Access, vol. 8, pp. 96495–96504, 2020, doi: 10.1109/ACCESS.2020.2995681.
[6] J. Bobulski and M. Kubanek, “Deep Learning for Plastic Waste Classification System,” Applied Computational Intelligence and Soft Computing, vol. 2021, pp. 1–7, May 2021, doi: 10.1155/2021/6626948.
[7] S. Poudel and P. Poudyal, “Classification of Waste Materials using CNN Based on Transfer Learning,” Proceedings of the 14th Annual Meeting of the Forum for Information Retrieval Evaluation, pp. 29–33, Dec. 2022, doi: 10.1145/3574318.3574345.
[8] K. Huang, H. Lei, Z. Jiao, and Z. Zhong, “Recycling Waste Classification Using Vision Transformer on Portable Device,” Sustainability, vol. 13, no. 21, p. 11572, Oct. 2021, doi: 10.3390/su132111572.
[9] L. Song, H. Zhao, Z. Ma, and Q. Song, “A new method of construction waste classification based on two-level fusion,” PLoS One, vol. 17, no. 12, p. e0279472, Dec. 2022, doi: 10.1371/journal.pone.0279472.
[10] M. Anas, N. Hikmah, and I. Aprilia, “Smart Trash Klasifikasi Sampah Otomatis Dengan Sensor Proximity Berbasis Arduino,” Jurnal FORTECH, vol. 3, no. 2, pp. 64–72, Jan. 2023, doi: 10.56795/fortech.v3i2.103.
[11] Q. Zhang, Q. Yang, X. Zhang, Q. Bao, J. Su, and X. Liu, “Waste image classification based on transfer learning and convolutional neural network,” Waste Management, vol. 135, pp. 150–157, Nov. 2021, doi: 10.1016/j.wasman.2021.08.038.
[12] J. Qin, C. Wang, X. Ran, S. Yang, and B. Chen, “A robust framework combined saliency detection and image recognition for garbage classification,” Waste Management, vol. 140, pp. 193–203, Mar. 2022, doi: 10.1016/j.wasman.2021.11.027.
[13] B. Kang and C.-S. Jeong, “ARTD-Net: Anchor-Free Based Recyclable Trash Detection Net Using Edgeless Module,” Sensors, vol. 23, no. 6, p. 2907, Mar. 2023, doi: 10.3390/s23062907.
[14] L. Wang, C. Wang, Z. Sun, S. Cheng, and L. Guo, “Class Balanced Loss for Image Classification,” IEEE Access, vol. 8, pp. 81142–81153, 2020, doi: 10.1109/ACCESS.2020.2991237.
[15] M. Saini and S. Susan, “Bag-of-Visual-Words codebook generation using deep features for effective classification of imbalanced multi-class image datasets,” Multimed Tools Appl, vol. 80, no. 14, pp. 20821–20847, Jun. 2021, doi: 10.1007/s11042-021-10612-w.
[16] Q. Jin, M. Yuan, H. Wang, M. Wang, and Z. Song, “Deep active learning models for imbalanced image classification,” Knowl Based Syst, vol. 257, p. 109817, Dec. 2022, doi: 10.1016/j.knosys.2022.109817.
[17] L. Li, L. Han, H. Hu, Z. Liu, and H. Cao, “Standardized object-based dual CNNs for very high-resolution remote sensing image classification and standardization combination effect analysis,” Int J Remote Sens, vol. 41, no. 17, pp. 6635–6663, Sep. 2020, doi: 10.1080/01431161.2020.1742946.
[18] S. S. Mullick, S. Datta, S. G. Dhekane, and S. Das, “Appropriateness of performance indices for imbalanced data classification: An analysis,” Pattern Recognit, vol. 102, p. 107197, Jun. 2020, doi: 10.1016/j.patcog.2020.107197.
[19] B. Kim, Y. Ko, and J. Seo, “Novel regularization method for the class imbalance problem,” Expert Syst Appl, vol. 188, p. 115974, Feb. 2022, doi: 10.1016/j.eswa.2021.115974.
[20] M. Juez-Gil, Á. Arnaiz-González, J. J. Rodríguez, and C. García-Osorio, “Experimental evaluation of ensemble classifiers for imbalance in Big Data,” Appl Soft Comput, vol. 108, p. 107447, Sep. 2021, doi: 10.1016/j.asoc.2021.107447.
[21] Z. Wang et al., “A Comprehensive Survey on Data Augmentation,” May 2024, [Online]. Available: http://arxiv.org/abs/2405.09591
[22] A. Mumuni and F. Mumuni, “Data augmentation: A comprehensive survey of modern approaches,” Array, vol. 16, p. 100258, Dec. 2022, doi: 10.1016/j.array.2022.100258.
[23] J. Su, X. Yu, X. Wang, Z. Wang, and G. Chao, “Enhanced transfer learning with data augmentation,” Eng Appl Artif Intell, vol. 129, p. 107602, Mar. 2024, doi: 10.1016/j.engappai.2023.107602.
[24] K. M. Kahloot and P. Ekler, “Algorithmic Splitting: A Method for Dataset Preparation,” IEEE Access, vol. 9, pp. 125229–125237, 2021, doi: 10.1109/ACCESS.2021.3110745.
[25] V. Lumumba, D. Kiprotich, M. Mpaine, N. Makena, and M. Kavita, “Comparative Analysis of Cross-Validation Techniques: LOOCV, K-folds Cross-Validation, and Repeated K-folds Cross-Validation in Machine Learning Models,” American Journal of Theoretical and Applied Statistics, vol. 13, no. 5, pp. 127–137, Oct. 2024, doi: 10.11648/j.ajtas.20241305.13.
[26] A. Taherkhani, G. Cosma, and T. M. McGinnity, “AdaBoost-CNN: An adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning,” Neurocomputing, vol. 404, pp. 351–366, Sep. 2020, doi: 10.1016/j.neucom.2020.03.064.
[27] S. F. Ismael, K. Kayabol, and E. Aptoula, “Unsupervised Domain Adaptation for the Semantic Segmentation of Remote Sensing Images via One-Shot Image-to-Image Translation,” IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023, doi: 10.1109/LGRS.2023.3281458.
[28] S. G. Brucal et al., “Development of Tomato Leaf Disease Detection using Single Shot Detector (SSD) Mobilenet V2,” International Journal of Computing Sciences Research, vol. 7, pp. 1857–1869, Jan. 2023, doi: 10.25147/ijcsr.2017.001.1.136.
[29] M. Abu-zanona, S. Elaiwat, S. Younis, N. Innab, and M. M. Kamruzzaman, “Classification of Palm Trees Diseases using Convolution Neural Network,” International Journal of Advanced Computer Science and Applications, vol. 13, no. 6, 2022, doi: 10.14569/IJACSA.2022.01306111.
[30] S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah, “Transformers in Vision: A Survey,” ACM Comput Surv, vol. 54, no. 10s, pp. 1–41, Jan. 2022, doi: 10.1145/3505244.
[31] A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics, vol. 17, no. 1, pp. 168–192, Jan. 2021, doi: 10.1016/j.aci.2018.08.003.
[32] D. Chicco and G. Jurman, “The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,” BMC Genomics, vol. 21, no. 1, p. 6, Dec. 2020, doi: 10.1186/s12864-019-6413-7.
[33] L. Luceri, S. Giordano, and E. Ferrara, “Detecting Troll Behavior via Inverse Reinforcement Learning: A Case Study of Russian Trolls in the 2016 US Election,” Proceedings of the International AAAI Conference on Web and Social Media, vol. 14, pp. 417–427, May 2020, doi: 10.1609/icwsm.v14i1.7311.
[34] R. Risfendra, G. F. Ananda, and H. Setyawan, “Deep Learning-Based Waste Classification with Transfer Learning Using EfficientNet-B0 Model,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 4, pp. 535–541, Aug. 2024, doi: 10.29207/resti.v8i4.5875.
[35] G. White, C. Cabrera, A. Palade, F. Li, and S. Clarke, “WasteNet: Waste Classification at the Edge for Smart Bins,” Jun. 2020, [Online]. Available: http://arxiv.org/abs/2006.05873
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Raffa Adhi Kumala, Christy Atika Sari, Eko Hari Rachmawanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
