Implementation Of The Eco Cycle Classifier Deep Neural Network (EECDN-Net) Model For Image-Based Waste Classification

Abstract

Waste management is a global challenge that demands effective solutions, especially in classification and recycling processes. This study presents the development of an Eco Cycle Classifier Deep Neural Network (ECCDN-Net) model based on deep learning for image-based waste classification. The model integrates the DenseNet201 and ResNet18 architectures to improve visual feature extraction and reduce the vanishing gradient problem. The dataset used is TrashNet, which contains 2,527 images across six waste categories. Training was conducted over 50 epochs, utilizing data augmentation and class balancing to address the imbalanced data. Results show that ECCDN-Net achieved a validation accuracy of 87.75% and an average F1-score of 0.88. The confusion matrix reveals that the model performs well in recognizing most classes, although it faces difficulty distinguishing categories with high visual similarity, such as plastic and glass. This research demonstrates that ECCDN-Net effectively provides accurate waste classification and could serve as a promising solution for more adaptive and sustainable automatic waste sorting.