AI-Driven Smart Waste Classification Model for Sustainable Practices in the RMG Industry
Keywords:
Deep learning, Image classification, Long-term sustainability, Machine learning, RMG sector, Waste classificationAbstract
Waste management has always been a very complex issue in the ready-made garment (RMG) sector. Each year, almost 92 million tons of textile waste are generated globally. As a result, an effective and efficient waste management system is genuinely required in order to ensure long-term sustainability. This study presents an efficient waste sorting model for the RMG industry. This framework used image-based classification models to uniquely identify different types of common waste, including fabric scraps, buttons, zippers, threads and labels. Various machine learning and deep learning models, including Convolutional Neural Networks (CNN), Transfer Learning models (InceptionV3, ResNet50V2, VGG16), Decision Trees, Support Vector Machines (SVM), Random Forest, and k-Nearest Neighbours (KNN), are being used in this study. The performances of these algorithms were also compared in this research. The datasets were collected from three different major RMG manufacturers in Bangladesh which were further enriched with data augmentation techniques. The categorical cross-entropy loss, the Adam optimizer, and dropout regularization were incorporated in the model training part to enhance generalization. After the evaluation, the final result shows that InceptionV3 achieved the highest classification accuracy at 90.00%, VGG16 at 88.81%, and ResNet50V2 at 87.99%, while the basic CNN model achieved 57.30% accuracy. This result highlights the fact that AI integration has become a dire need in the RMG industry to ensure long-term sustainability.
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