Proceedings Article | 14 June 2023
KEYWORDS: Data modeling, Performance modeling, Machine learning, Education and training, Deep learning, Motion detection, Image classification, Light emitting diodes, Instrument modeling, Motion models
Segregating waste accurately at an individual level is paramount for efficient waste management, especially considering the staggering 268 million tons of waste generated each year in the USA, a large portion of which is recyclable. To address this issue, we developed a Smart Waste Sorter (SWS), which is a portable device that can be placed on any waste bin. It uses deep learning models to identify whether a piece of waste is a battery, recyclable, compostable, or recyclable, or trash, and provides a real time alert if the user is about to dispose of the item incorrectly. To develop the SWS image classification model, we utilized a dataset of 4,122 images, obtained from a combination of publicly available and manually collected images from households over several months. We experimented with four models of varying sizes: VGG16, EfficientNetB1, MobileNetV2, and ResNet50, to investigate whether a smaller model could achieve comparable performance, given that our device is portable and requires a compact model that can operate on limited memory without internet connectivity. Our experiments showed that ResNet50 achieved the highest validation and test accuracy of 77.91% and 96.39% respectively over four categories, suggesting that smaller models can be effective. Our results demonstrate the potential of the SWS to improve real-time waste segregation at the individual level, while considering practical constraints for implementation. The proposed solution utilizes a Raspberry Pi to detect motion, capture images and classify them. Our solution provides an effective, practical, and low-cost method for accurately segregating waste and contributing to sustainable waste management.
