Classification Model for Waste Materials in Residential Areas Computer Vision Project

Thesis Project

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Description

The thesis project aims to develop a multi-label classification model that can identify residential waste materials along with displaying their target categories (i.e., recyclable, non-recyclable, compostable, e-waste, and medical waste) in real-time for efficient waste management using deep learning techniques and sensor fusion.

The waste materials will be classified according to the waste segregation advisory of the DENR under five (5) waste categories: recyclable, non-recyclable, compostable, e-waste, and medical waste. The study will involve identifying the following twelve (12) classes:

Bottle
Tin Can
Cardboard
Paper
Soft Plastic
Tetra Packs
Leftovers
Food Peeling
Battery
Gadgets
Face Mask
Gloves

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LICENSE

CC BY 4.0

If you use this dataset in a research paper, please cite it using the following BibTeX:

                        @misc{
                            classification-model-for-waste-materials-in-residential-areas_dataset,
                            title = { Classification Model for Waste Materials in Residential Areas Dataset },
                            type = { Open Source Dataset },
                            author = { Thesis Project },
                            howpublished = { \url{ https://universe.roboflow.com/thesis-project-sacr3/classification-model-for-waste-materials-in-residential-areas } },
                            url = { https://universe.roboflow.com/thesis-project-sacr3/classification-model-for-waste-materials-in-residential-areas },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-12-31 },
                            }