Studying Links Between Litter and Socio-Economic Factors with Computer Vision
Blog post from Roboflow
In 2021, Gary Blackwood, a postgraduate student at the University of Glasgow, conducted a study using computer vision to explore correlations between litter amounts in Glasgow and socio-economic factors such as employment rate, income, and education levels. By employing a YOLOv5s object detection model trained on thousands of annotated Google Street View images, Gary aimed to create a reliable data source for analyzing litter distribution across various city areas. His findings, derived from regression analyses, indicated no statistically significant relationship between socio-economic factors and litter rates, although limitations such as hardware constraints and image resolution might have affected the outcomes. Despite these challenges, the study highlighted computer vision's potential in urban policy-making, proposing that the model could be scaled and adapted for various applications, including improved trash collection strategies in Glasgow and potentially other cities.
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