Multi-class Waste Classification Using Convolutional Neural Network

Abstract

This study explores a method of waste classification using deep learning, specifically employing the Convolutional Neural Network (CNN). This research involves the creation of a unique dataset, a hybrid of publicly accessible data and a newly compiled collection of images across 13 waste classes: paper, glass, wood, metal, clothes, PCB e-waste, non-PCB e-waste, PET, HDPE, LDPE, PP, PVC, and PS. The development of the CNN model was approached in two ways: transfer learning and full learning. In the transfer learning approach, two pre-trained models, MobileNetV2 and DenseNet121, were utilized. While in the full learning approach, the architecture is constructed using the sequential method. The experimental results indicated that the DenseNet121 transfer learning model outperformed others, achieving an impressive accuracy of 95.2% and an average F-1 score of 0.95 on test data. This was closely followed by the MobileNetV2 transfer learning model, which attained an accuracy of 92% and an average F-1 score of 0.92. In comparison, the full learning model reached an accuracy of 65% and an average F-1 score of 0.65. Generally, transfer learning models yielded more optimal results than those full learning model. This efficiency can be attributed to the pre-existing knowledge in the transfer learning models, which eliminates the need to learn input patterns from the ground up. However, it's important to note that the dataset size of 4586 images across 13 classes may not be sufficient for developing a robust machine learning model from scratch.

Keywords

• Solid waste
• Deep learning
• Convolutional neural network
• Classification
• Transfer learning
• Full learning