A deep learning approach to identify unhealthy advertisements in street view images

TL;DR

This paper presents a deep learning workflow to automatically detect and classify unhealthy advertisements in street view images, aiding policy enforcement and social inequality analysis.

Contribution

It introduces a new dataset and a deep learning method for automatic classification of unhealthy ads in street-level images, addressing manual data collection challenges.

Findings

Higher prevalence of food ads in deprived areas

More ads targeted at students in certain locations

Effective automatic classification of unhealthy advertisements

Abstract

While outdoor advertisements are common features within towns and cities, they may reinforce social inequalities in health. Vulnerable populations in deprived areas may have greater exposure to fast food, gambling and alcohol advertisements encouraging their consumption. Understanding who is exposed and evaluating potential policy restrictions requires a substantial manual data collection effort. To address this problem we develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street-level images. We introduce the Liverpool 360 Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 25,349, 360 degree, street-level images collected via cycling with a GoPro Fusion camera, recorded Jan 14th - 18th 2020. 10,106 advertisements were identified and classified as food (1335), alcohol (217), gambling (149) and other (8405) (e.g., cars and broadband). We find evidence of social inequalities with a larger proportion of food advertisements located within deprived areas and those frequented by students. Our project presents a novel implementation for the incidental classification of street view images for identifying unhealthy advertisements, providing a means through which to identify areas that can benefit from tougher advertisement restriction policies for tackling social inequalities.