Garbage contains waste material disposed of by people. Waste segregation into biodegradable and non-biodegradable diminishes the substantial lumps of waste to get dumped at the landfill which makes it less expensive and for individuals and the environment. In this paper, a computer vision approach is proposed to classify waste into biodegradable and non-biodegradable. This paper proposes a deep neural network framework that uses Convolutional Neural Network (CNN) for extraction of features. A new data set was built for testing the execution of the anticipated model as there was no means of the exact and precise data set. The data set is analyzed with our proposed framework and with other machine learning algorithms, such as Support Vector Machine (SVM), K-Nearest Neighborhood (KNN), Random Forest (RF), AdaBoost and Logistic Regression. The proposed approach produces a classification accuracy of 98.33% outperforming the state-of-art algorithms.
Volume 11 | 03-Special Issue
Pages: 1805-1811