Performance analysis of MobileNetV2 based automatic waste classification using transfer learning
DOI:
https://doi.org/10.35335/mandiri.v14i1.451Keywords:
CPU-based AI, Deep Learning, MobileNetV2, Transfer Learning, Waste ClassificationAbstract
The significant increase in global waste requires innovative and accessible solutions, which aligns with Sustainable Development Goal (SDG) 12, which focuses on reducing the environmental impact of human activities. Automatic waste sorting using Computer Vision and Deep Learning offers a promising alternative to labor-intensive and risky manual methods. This study presents the design, implementation, and comprehensive performance analysis of an automated waste classification system, with a specific focus on evaluating its feasibility on hardware without specialized GPU accelerators. By leveraging transfer learning on a lightweight Convolutional Neural Network (CNN) architecture, MobileNetV2, a model was trained to classify six common waste categories: cardboard, glass, metal, paper, plastic, and other waste. The public “Garbage Classification” dataset from Kaggle, consisting of 2,527 images, was used as the basis for training and validation. The experiment was conducted using the tensorflow-cpu library, which does not require a dedicated GPU accelerator. After 10 training epochs, the model achieved a significant validation accuracy of 86.73%. Computational performance analysis showed an efficient average training time of 31.17 seconds per epoch and a fast average inference time of 14.47 milliseconds per image (~69 FPS) on the validation dataset. These findings demonstrate the feasibility of developing an effective AI-based waste classification system on hardware without a GPU accelerator, providing a realistic performance benchmark for the development of low-cost smart bins with embedded waste sorting solutions in the future, thereby contributing to sustainable waste management practices.
References
Ali, M. S., Miah, M. S., Haque, J., Rahman, M. M., & Islam, M. K. (2021). An enhanced technique of skin cancer classification using deep convolutional neural network with transfer learning models. Machine Learning with Applications, 5(April), 100036. https://doi.org/10.1016/j.mlwa.2021.100036
Almghraby, M., & Elnady*, A. O. (2021). Face Mask Detection in Real-Time using MobileNetv2. International Journal of Engineering and Advanced Technology, 10(6), 104–108. https://doi.org/10.35940/ijeat.f3050.0810621
Dehghani, M. H., Omrani, G. A., & Karri, R. R. (2021). Solid Waste—Sources, Toxicity, and Their Consequences to Human Health. Soft Computing Techniques in Solid Waste and Wastewater Management, January 2021, 205–213. https://doi.org/10.1016/B978-0-12-824463-0.00013-6
Dong, Z., Yuan, L., Yang, B., Xue, F., & Lu, W. (2025). Benchmarking computer vision models for automated construction waste sorting. Resources, Conservation and Recycling, 213, 108026. https://doi.org/https://doi.org/10.1016/j.resconrec.2024.108026
Fan, H., Dong, Q., Guo, N., Xue, J., Zhang, R., Wang, H., & Shi, M. (2023). Raspberry Pi-based design of intelligent household classified garbage bin. Internet of Things, 24, 100987. https://doi.org/https://doi.org/10.1016/j.iot.2023.100987
Fang, B., Yu, J., Chen, Z., Osman, A. I., Farghali, M., Ihara, I., Hamza, E. H., Rooney, D. W., & Yap, P. S. (2023). Artificial intelligence for waste management in smart cities: a review. In Environmental Chemistry Letters (Vol. 21, Issue 4). Springer International Publishing. https://doi.org/10.1007/s10311-023-01604-3
Fotovvatikhah, F., Ahmedy, I., Noor, R. M., & Munir, M. U. (2025). A Systematic Review of AI-Based Techniques for Automated Waste Classification. Sensors, 25(10). https://doi.org/10.3390/s25103181
Gao, Z., Tian, Y., Lin, S.-C., & Lin, J. (2025). A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical Field. Applied and Computational Engineering, 133(1), 90–96. https://doi.org/10.54254/2755-2721/2025.20605
George Tchobanoglous, H. T., & Vigil, S. A. (1993). Integrated Solid Waste Management: Engineering Principles and Management Issues. McGraw-Hill.
Gyawali, D., Regmi, A., Shakya, A., Gautam, A., & Shrestha, S. (2020). Comparative Analysis of Multiple Deep CNN Models for Waste Classification. http://arxiv.org/abs/2004.02168
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
Hosna, A., Merry, E., Gyalmo, J., Alom, Z., Aung, Z., & Azim, M. A. (2022). Transfer learning: a friendly introduction. Journal of Big Data, 9(1). https://doi.org/10.1186/s40537-022-00652-w
Hussain, D., Ismail, M., Hussain, I., Alroobaea, R., Hussain, S., & Ullah, S. S. (2022). Face Mask Detection Using Deep Convolutional Neural Network and MobileNetV2-Based Transfer Learning. Wireless Communications and Mobile Computing, 2022. https://doi.org/10.1155/2022/1536318
Iman, M., Arabnia, H. R., & Rasheed, K. (2023). A Review of Deep Transfer Learning and Recent Advancements. Technologies, 11(2), 1–14. https://doi.org/10.3390/technologies11020040
Jin, S., Yang, Z., Królczykg, G., Liu, X., Gardoni, P., & Li, Z. (2023). Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling. Waste Management, 162, 123–130. https://doi.org/https://doi.org/10.1016/j.wasman.2023.02.014
Kaza, S., Yao, L. C., Bhada-Tata, P., & Van Woerden, F. (2018). What a Waste 2.0: A Global Snapshot of Solid Waste Management to 2050 (Urban Deve). World Bank. https://doi.org/10.1596/978-1-4648-1329-0
Kementerian Lingkungan Hidup dan Kehutanan (KLHK). (n.d.). Sistem Informasi Pengelolaan Sampah Nasional (SIPSN). https://sipsn.kemenlh.go.id/sipsn/
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022). Transfer learning for medical image classification: a literature review. BMC Medical Imaging, 22(1), 1–13. https://doi.org/10.1186/s12880-022-00793-7
Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. ArXiv Preprint ArXiv:1412.6980.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
Lu, W., & Chen, J. (2022). Computer vision for solid waste sorting: A critical review of academic research. Waste Management, 142, 29–43. https://doi.org/https://doi.org/10.1016/j.wasman.2022.02.009
Ma, R., Wang, J., Zhao, W., Guo, H., Dai, D., Yun, Y., Li, L., Hao, F., Bai, J., & Ma, D. (2023). Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM. Agriculture (Switzerland), 13(1). https://doi.org/10.3390/agriculture13010011
Mao, W. L., Chen, W. C., Wang, C. T. C., Lin, Y. H., Tian, Y. Y., Feng, Y., Iman, M., Arabnia, H. R., Rasheed, K., He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., Neubig, G., Zhu, Z., Lin, K., Jain, A. K., Zhou, J., Kim, H. E., … Du, L. (2023). Fruit classification using attention-based MobileNetV2 for industrial applications. Waste Management, 135(1), 1–16. https://doi.org/10.1080/01621459.2022.2071278
Mao, W. L., Chen, W. C., Wang, C. T., & Lin, Y. H. (2021). Recycling waste classification using optimized convolutional neural network. Resources, Conservation and Recycling, 164(August 2020), 105132. https://doi.org/10.1016/j.resconrec.2020.105132
Nowakowski, P., & Pamuła, T. (2020). Application of deep learning object classifier to improve e-waste collection planning. Waste Management, 109, 1–9. https://doi.org/10.1016/j.wasman.2020.04.041
Pinto, G., Wang, Z., Roy, A., Hong, T., & Capozzoli, A. (2022). Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives. Advances in Applied Energy, 5(November 2021), 100084. https://doi.org/10.1016/j.adapen.2022.100084
Shahi, T. B., Sitaula, C., Neupane, A., & Guo, W. (2022). Fruit classification using attention-based MobileNetV2 for industrial applications. PLoS ONE, 17(2 February), 1–21. https://doi.org/10.1371/journal.pone.0264586
Sheng, T. J., Islam, M. S., Misran, N., Baharuddin, M. H., Arshad, H., Islam, M. R., Chowdhury, M. E. H., Rmili, H., & Islam, M. T. (2020). An Internet of Things Based Smart Waste Management System Using LoRa and Tensorflow Deep Learning Model. IEEE Access, 8, 148793–148811. https://doi.org/10.1109/ACCESS.2020.3016255
Sung, Y. (2022). VL-A DAPTER : Parameter-Efficient Transfer Learning for Vision-and-Language Tasks. 2, 5227–5237.
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1–9. https://doi.org/10.1109/CVPR.2015.7298594
Tian, Y., & Feng, Y. (2023). Transfer Learning Under High-Dimensional Generalized Linear Models. Journal of the American Statistical Association, 118(544), 2684–2697. https://doi.org/10.1080/01621459.2022.2071278
UN. (2015). Transforming our world: The 2030 Agenda for Sustainable Development. https://sdgs.un.org/2030agenda
Vinti, G., Bauza, V., Clasen, T., Medlicott, K., Tudor, T., Zurbrügg, C., & Vaccari, M. (2021). Municipal Solid Waste Management and Adverse Health Outcomes: A Systematic Review. International Journal of Environmental Research and Public Health, 18(8). https://doi.org/10.3390/ijerph18084331
Yang, M., & Thung, G. (2016). Classification of the TrashNet dataset (CS229 Proj). Stanford University.
Yong, L., Ma, L., Sun, D., & Du, L. (2023). Application of MobileNetV2 to waste classification. PLoS ONE, 18(3 March), 1–16. https://doi.org/10.1371/journal.pone.0282336
Zhang, Q., Yang, Q., Zhang, X., Bao, Q., Su, J., & Liu, X. (2021). Waste image classification based on transfer learning and convolutional neural network. Waste Management, 135(August), 150–157. https://doi.org/10.1016/j.wasman.2021.08.038
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Copyright (c) 2025 Ricy Firnando, Muhammad Ali Buchari, Anna Dwi Marjusalinah, Willy, Abdurahman, Rahmat Fadli Isnanto
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