Garbage Classification based on Dense Network (GCDN) using Transfer Learning and Modified Hyper Parameter
Keywords:
Garbage Classification, Waste Classification, Machine Learning, Deep Learning, Transfer Learning, Image AugmentationAbstract
Garbage classification plays a vital role in waste management and sustainability of the environment. Traditional methods of waste classification often depend on manual sorting, which is very time-consuming and prone to human errors which can lead to policy inadequacy by the government. In this paper, we proposed a deep learning-based DENSNET201 approach Garbage Classification based on Dense Network (GCDN) for garbage classification to automate and improve the accuracy of this process. Our method utilizes an additional layer of convolutional neural networks (CNNs) to classify garbage into 12 categories such as shoes, green-glass, paper, cardboard, battery, biological, plastic, metal, brown-glass, white-glass and trash. We have executed the different state of the art models of deep learning on a publicly available dataset comprising images of various types of garbage collected from diverse environments. We then employed image augmentation methods followed by transfer learning techniques to fine-tune pre-trained CNN models on this dataset. During the analysis of the results, we have achieved the high classification accuracy of training and validation phase 98.64% and 93.23% respectively. Experimental results demonstrate the effectiveness of our approach in accurately classifying garbage, even in challenging scenarios with diverse backgrounds and lighting conditions. Furthermore, we discuss the potential applications of our system in real-world waste management scenarios, including smart waste bins and recycling facilities, to streamline garbage sorting processes and promote environmental sustainability.
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