CNN Image Classiffcation on Military Pictures
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2018-06-18
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nl
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Abstract
This project is part of a pipeline under the title "Adopt a bullet" that
aims at gathering information of di erent weapon transports by using AItechniques.
One stage in this pipeline consists of identifying the information
that is relevant for solving this problem. To approach this stage I implemented
a convolutional neural network (CNN) and trained it on a large set
of images. The research question was, if it would be able to distinguish between
images depicting military armoury and those that are not reliably. In
this case, images of tanks have been used for training. After an initial training
over 10 epochs, an accuracy of 74.33% was achieved. A second, smaller
CNN was trained in an attempt to prevent over tting. This second CNN
achieved a nal accuracy of 82.05%. This is a good result, but over tting
still occurred. Further experimentation on its prevention as well as further
eld-testing of the CNN is recommended, for example by applying it to a
web-crawler.
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Faculteit der Sociale Wetenschappen