Specialization of an Existing Image Recognition Service Using a Neural Network

University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS); KTH/Skolan för elektroteknik och datavetenskap (EECS)

Abstract: To help combat the environmental impacts caused by humans this project is about investigating one way to simplify the waste management process. The idea is to use image recognition to identify what material the recyclable object is made of. A large data set containing labeled images of trash, called Trashnet, was analyzed using Google Cloud Vision. Since this API is not written for material detection specifically, a feed forward neural network was created using Tensorflow and trained with the output from Google Cloud Vision. Thus, the network learned how different word combinations from Google Cloud Vision implicated one of five different materials; glass, plastic, paper, metal and combustible waste. The network checked for 518 unique words in the input and ran them through two hidden layers with a size of 1000 nodes each, before having a one hot output layer. This neural network received an accuracy of around 60%, which beat Google Cloud Vision’s meager accuracy of around 30%. An application, with which the user can take pictures of the object he or she would like to recycle, could be developed with an educational purpose to let its user know what material the waste is made of, and with this information be able to throw the waste in the right bin.

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