Image classification on a smart bin for waste segregation and analytics
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Date
2026-06-01
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Publisher
Uganda Christian University
Abstract
This project addresses the problem of inefficient waste sorting methods currently in use, such as bins with well labelled compartments that rely on users to sort waste correctly. In addition, manual sorting by waste collectors at the point of collection is unsafe due to exposure to hazardous materials. There is also a lack of sufficient data to support effective waste management and decision-making by organizations such as Kampala Capital City Authority (KCCA) and
National Environment Management Authority (NEMA). To address these challenges, we developed a smart bin with three compartments for paper, plastics, and general waste. The system allows a user to dispose of a single item of waste, after which the bin automatically sorts it into the correct compartment. At the same time, data is collected and sent to a dashboard for analysis of waste trends. The system works by using a camera to capture an image of the waste item. The image is then processed by a VGG16 deep learning model, which classifies the type of waste based on its visual features. The classification result is used to guide the sorting mechanism, and the data is transmitted to an application for further analysis. The backend is built with Node.js and Express, using MongoDB for data storage, while the frontend dashboard is developed with React and Tailwind CSS. The model was trained on a custom dataset of over 500 waste images collected at Uganda Christian University, supplemented by transfer learning from the VGG16 architecture pre trained on ImageNet. Testing demonstrated a classification accuracy exceeding 90% for the targeted waste categories. The integrated system successfully sorts waste into the correct compartments and provides real-time analytics on waste disposal patterns. This solution contributes to improved waste segregation at the source, reduces occupational hazards for waste workers, and provides data-driven insights for institutional waste management.
Description
Undergraduate