Garbage Detection Using Deep Learning Methods (GD-DLM)

In today’s expanding and densely populated world, it’s crucial to design an automatic intelligent garbage sorter machine that uses advanced sensors. Garbage picture classification is a fundamental computer vision problem that must be solved before sensors can be included in this system. This research presents a model for autonomous trash classification using deep learning that can be applied in high-tech garbage sorting equipment. The 2,527 photos in the rubbish dataset are categorized into six types: trash, cardboard, glass, metal, paper, and plastic. The next step is the creation of GD-DLM, a deep learning model for garbage categorization that is an upgrade from Xception and DenseNet121 models. At last, the tests are run to evaluate GD-DLM against the best-of-breed approaches to garbage classification. The suggested Xception and DenseNet-121 models scored 92.11% and 88.63%, respectively, compared to the baseline accuracy.