Trash and recyclable material identification using convolutional neural networks (CNN)

WCU Author/Contributor (non-WCU co-authors, if there are any, appear on document)
Rumana Sultana (Creator)
Western Carolina University (WCU )
Web Site:
Robert Adams

Abstract: The aim of this research is to improve municipal trash collection using image processing algorithms and deep learning technologies for detecting trash in public spaces. This research will help to improve trash management systems and create a smart city. Two Convolutional Neural Networks (CNN), both based on the AlexNet network architecture, were developed to search for trash objects in an image and separate recyclable items from the landfill trash objects, respectively. The two-stage CNN system was first trained and tested on the benchmark TrashNet indoor image dataset and achieved great performance to prove the concept. Then the system was trained and tested on outdoor images taken by the authors in the intended usage environment. Using the outdoor image dataset, the first CNN achieved a preliminary 93.6% accuracy to identify trash and non-trash items on an image database of assorted trash items. A second CNN was then trained to distinguish trash that will go to a landfill from the recyclable items with an accuracy ranging from 89.7% to 93.4% and overall, 92%. A future goal is to integrate this image processing-based trash identification system in a smart trashcan robot with a camera to take real-time photos that can detect and collect the trash all around it.

Additional Information

Language: English
Date: 2020
AlexNet, CNN, Deep Learning, Image Processing, LabVIEW, MATLAB
CNN International
Neural networks (Computer science)
Machine learning
Image processing.

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