Authors : Usman Ali

Volume/Issue : Volume 11 - 2026, Issue 9 - September

Google Scholar : https://tinyurl.com/mrnxhdy9

DOI : https://doi.org/10.38124/ijisrt/26sep1189

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Automatic waste classification is an important challenge for smart cities and intelligent recycling systems. Traditional manual sorting methods are time-consuming and inefficient, while existing computer vision approaches often require computationally expensive models. This research proposes an efficient convolutional neural network (CNN) framework for automatic waste classification from images. The proposed model learns visual features such as shape, texture, and color patterns to classify different waste categories. A customized dataset containing multiple waste classes is used for training and evaluation. The model performance is assessed using accuracy, precision, recall, and F1-score metrics. The proposed lightweight CNN architecture achieves reliable classification performance while reducing computational requirements, making it suitable for real-time deployment in smart recycling systems.

Keywords : Waste Classification, CNN, Deep Learning, Computer Vision, Smart City.

References :

1. I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, MA, USA, 2016.
2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.
3. K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016.
4. M. Yang and G. Thung, “Classification of Trash for Recyclability Status,” Stanford University CS229 Machine Learning Project Report, 2016.
5. A. Adedeji and Z. Wang, “Intelligent Waste Classification System Using Deep Learning Convolutional Neural Network,” Procedia Manufacturing, vol. 35, pp. 607–612, 2019.
6. F. M. Al-Masni, M. A. Al-Antari, M. T. Choi, S. M. Han, and T. S. Kim, “Skin Lesion Classification in Dermoscopy Images via Deep Convolutional Neural Networks,” IEEE Access, vol. 7, pp. 161193–161202, 2019.
7. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in International Conference on Learning Representations (ICLR), 2015.
8. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1251–1258, 2017.
9. M. Tan and Q. V. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” in Proceedings of the International Conference on Machine Learning (ICML), pp. 6105–6114, 2019.
10. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708, 2017.
11. D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” in International Conference on Learning Representations (ICLR), 2015.
12. R. Padilla, W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. da Silva, “A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit,” Electronics, vol. 10, no. 3, pp. 279, 2021.
13. A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” arXiv preprint arXiv:2004.10934, 2020.
14. J. Deng, W. Dong, R. Socher, L. Li, K. Li, and L. Fei-Fei, “ImageNet: A Large-Scale Hierarchical Image Database,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255, 2009.
15. G. Jocher et al., “YOLO by Ultralytics: Real-Time Object Detection with Deep Learning,” 2023.