Abstract
Abstract: Managing solid waste through recycling is a crucial measure to mitigate adverse effects like sanitation and health issues arising from excessive landfill usage. However, the intricacies and costs associated with sorting solid waste pose challenges to recycling efforts. To streamline this process, our study proposes a Deep Learning approach employing computer vision to automatically recognize and categorize waste into five primary types: plastic, metal, paper, cardboard, and glass. Our conceptual system entails an automated recycling bin that opens its lid corresponding to the identified waste type. The primary focus of our work lies in the development and optimization of Machine Learning algorithms for efficient waste identification. We utilized pre-existing images to train a minimum of 12 variants of the Convolutional Neural Network (CNN) algorithm across three classifiers: Support Vector Machine (SVM), Sigmoid, and SoftMax. Our findings reveal that the VGG19 model with a SoftMax classifier achieves an accuracy of approximately 88%.
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