A Reliable and Robust Deep Learning Model for Effective Recyclable Waste Classification

• Published in: IEEE access 2024, Vol.12, p.13809-13821
• Main Authors: Hossen, Md. Mosarrof, Majid, Molla E., Kashem, Saad Bin Abul, Khandakar, Amith, Nashbat, Mohammad, Ashraf, Azad, Hasan-Zia, Mazhar, Kunju, Ali K. Ansaruddin, Kabir, Saidul, Chowdhury, Muhammad E. H.
• Format: Article
• Language: English
• Subjects: Automation; Biological system modeling; Cardboard; Categories; Classification; convolutional neural network (CNN); Convolutional neural networks; Deep learning; Image classification; Model accuracy; Modernization; multi-label classification; Recycling; Sorting; Training; waste classification; Waste management
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3354774

In response to the growing waste problem caused by industrialization and modernization, the need for an automated waste sorting and recycling system for sustainable waste management has become ever more pressing. Deep learning has made significant advancements in image classification, making it ideally suited for waste sorting applications. This application depends on the development of a suitable deep learning model capable of accurately categorizing various categories of waste. In this study, we present RWC-Net (recyclable waste classification network), a novel deep learning model designed for the classification of six distinct waste categories using the TrashNet dataset of 2,527 images of waste. The performance of our model is subjected to intensive quantitative and qualitative evaluations and is compared to various state-of-art waste classification techniques. The proposed model outperformed several state-of-the-art models by obtaining a remarkable overall accuracy rate of 95.01 percent. In addition, it receives high F1-scores for each of the six waste categories: 97.24% for cardboard, 96.18% for glass, 94% for metal, 95.73% for paper, 93.67% for plastic, and 88.55% for litter. The reliability of the model is demonstrated qualitatively through the saliency maps generated by Score-CAM (class activation mapping) model, which provide visual insights into its performance across various waste categories. These results highlight the model's accuracy and demonstrate its potential as an effective automated waste classification and management solution.