Authors : M. Mudasar Azeem; Syed Anwaar Mehdi; Muhammad Ali Shahid; Mubasher Hussain; Muhammad Adnan; Spogmai; Bilal Shabbir Qaisar

Volume/Issue : Volume 10 - 2025, Issue 9 - September

Google Scholar : https://tinyurl.com/2dea5bn2

Scribd : https://tinyurl.com/mry9r5sr

DOI : https://doi.org/10.38124/ijisrt/25sep396

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Abstract : 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.

Keywords : Machine Learning; Resnet50v2; Trauma; Medical Images.

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