A Garbage Profiling System Using Mask R-CNN Deep Learning Algorithm

In this paper, a model developed using Mask R-CNN to aid the garbage profiling process has been presented. This model identifies plastic waste from garbage; following which data analysis is done to present the cities having high levels of plastic usage. Using this data, the government will know in which cities action must be taken to handle the usage of plastic materials. Today, even when separate bins are present for all types of waste, we end up having mixed waste in the majority of the bins. If waste is not segregated at the place where it is generated, it won't be recycled and may end up being in the landfill for a considerable amount of time causing several problems. The dataset used for this study was obtained from Kaggle and a few external sources. After cleaning and labeling the data, the model was trained using the Mask R-CNN algorithm to classify plastic waste. To make the model user-friendly, it was deployed on a website and the results were visualized using Python's Pygal library. The main functions of the website are: (1) Present areas where usage of plastic is high and segregation is not happening properly (2) Present the amount of plastic usage in a given time frame. (3) Given an input image, generate the amount of plastic and non-plastic content in the image.