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Visual pollution is an impairment on an individual's ability to enjoy their surroundings. It usually takes the form of a messy and chaotic environment that can cause overstimulation of the visual senses. This includes trash, advertisements, construction, electric cables, and similar objects. Convolutional Neural Networks (CNNs) are a form of artificial intelligence that use supervised learning to process and classify images. In this research, a CNN processed images of city streets and classified them as polluted or not polluted based on the visual characteristics that it learned from during its training period. The CNN achieved a training accuracy of 98% and a validation accuracy of 80%.
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Vogelpohl, Jacob and Elgazzar, Heba, "Visual Pollution Classification using Convolutional Neural Networks" (2023). 2023 Celebration of Student Scholarship - Poster Presentations. 21.
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