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Advisor(s)
Abstract(s)
This work is a part of an ongoing study to substitute the identification of waste containers
via radio-frequency identification. The purpose of this paper is to propose a method of identification
based on computer vision that performs detection using images, video, or real-time video capture
to identify different types of waste containers. Compared to the current method of identification,
this approach is more agile and does not require as many resources. Two approaches are employed,
one using feature detectors/descriptors and other using convolutional neural networks. The former
used a vector of locally aggregated descriptors (VLAD); however, it failed to accomplish what was
desired. The latter used you only look once (YOLO), a convolutional neural network, and reached an
accuracy in the range of 90%, meaning that it correctly identified and classified 90% of the pictures
used on the test set.
Description
Keywords
Waste container Object detection VLAD Convolutional neural networks YOLO
Citation
Publisher
MDPI