DenseNet201-Based Waste Material Classification Using Transfer Learning Approach

Abstract

This paper explores the application of deep learning models in waste material classification, motivated by the urgent need for efficient waste management practices to address environmental sustainability concerns. Drawing parallels with the success of deep learning in healthcare domains, the study investigates the effectiveness of various deep learning architectures for waste material classification. The DenseNet201 model is proposed and compared with various deep learning models such as ResNet, MobileNetV2, AlexNet, and GoogleNet. Experimental results demonstrate that DenseNet201 achieves superior accuracy, average recall, and average precision, making it the most effective model for waste material classification. The dense connectivity and feature aggregation capabilities of DenseNet201 contribute to its outstanding performance, showcasing its potential for enhancing waste management processes.