Identification and Classification of Waste using CNN in Waste Management
The study compared the categorization of three waste categories using a CNN and a Support Vector Machine and concluded that the CNN should attain higher accuracy levels than the SVM once these challenges are overcome.
Abstract
Convolutional neural networks (CNNs) are being used in this paper to investigate waste classification. The study compared the categorization of three waste categories (plastic, paper, and metal) using a CNN and a Support Vector Machine (SVM). The results showed that while the SVM had an accuracy of 94.8%, the CNN's accuracy was lower at 83%. Another study divided waste into six categories and showed that the classification accuracy was 22% with a CNN, while the SVM had a test accuracy of 63%. The authors noted challenges in optimizing the hyperparameters, which prevented the CNN from being trained to its full potential. However, they concluded that the CNN should attain higher accuracy levels than the SVM once these challenges are overcome. The authors also used two popular CNN architectures, VGG16 and FastNet-34, in their proposed model for waste classification. The dataset used for the model was provided by Kaggle and consisted of 22564 waste photos divided into recyclable (R) and organic (O) categories. The results of the proposed model showed improved accuracy in waste classification compared to previous studies. The study highlights the potential for the use of CNNs in the field of waste management but highlights the importance of overcoming challenges in hyperparameter optimization for higher computer vision accuracy.