Enhancing waste management through municipal solid waste classification: a convolutional neural network approach

Abstract

The escalation of population, economic expansion, and industrialization has resulted in an increase in waste production. This has made waste management more challenging and has resulted in environmental deterioration, negatively impacting the quality of life. Recycling, reducing, and reusing are viable methods to eradicate the escalating waste issue, requiring the appropriate classification of municipal solid waste. This study focuses on comparing six advanced waste classification systems that employ a pre-trained convolutional neural network (CNN) designed to recognize twelve distinct categories of municipal waste. It has been determined that DarkNet53 is the most effective classifier among these six models. To assess the effectiveness of each waste classifier, the confusion matrix, precision, recall, F1 score, the area under the receiver operating characteristic curve, and the loss function are examined. It has been found that DarkNet53 has an F1 score of 98.7% and validation accuracy of 99%, respectively. The suggested approach will be useful in promoting garbage recovery and reuse in the direction of a circular and sustainable economy.