Intelligent solid waste classification system using combination of image processing and machine learning models

Abstract

Solid waste is a major issue in all countries around the world. Solid waste classification and segregation prior to reuse, recycling or recovery is an important step toward sustainable waste management. Traditional manual sorting of solid waste is a labour intensive process that may pose health risks to the workers. Currently, automated classification of solid waste using machine learning techniques are widely applied. This study is aiming to develop an automated waste classification model by testing traditional and deep machine learning models. To achieve that, both open (Trashnet) and generated datasets were used in the model training and testing. The study results showed relatively low prediction capability of the traditional machine learning models like Random Forest (RF) and Support Vector Machine (SVM) as compared to the deep machine learning Convolutional Neural Network (CNN). The testing of the three models on a combined data set of Trashnet with local garbage data set resulted in accuracy of 62.5% for SVM, 72.0% for RF and 92.7% for CNN. JONET deep learning model has been developed using a combination of pre-trained base model (DenseNet 201) with a new architicture that contains a fully connected layer in the classification stage with 1024 neurons. The model is capable to identify six classes of solid waste items with various accuracies. When tested on the Trashnet, the accuracy was 96.06%, while testing on the local garbage dataset gave an accuracy of 94.40%. JONET has been tested also on multi object images which gave an acceptable prediction accuracy.

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