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



Palmer, Gregory, Green, Mark, Boyland, Emma ORCID: 0000-0001-8384-4994, Vasconcelos, Yales Stefano Rios, Savani, Rahul ORCID: 0000-0003-1262-7831 and Singleton, Alex
A deep learning approach to identify unhealthy advertisements in street view images.

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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 degree Street View (LIV360SV) dataset for evaluating our workflow. The dataset contains 26,645, 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 and children carrying excess weight. 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.

Item Type: Article
Additional Information: 13 pages, 7 figures, 1 table. Currently being reviewed for Nature Scientific Reports
Uncontrolled Keywords: cs.CY, cs.CY, cs.LG
Depositing User: Symplectic Admin
Date Deposited: 28 Sep 2020 08:04
Last Modified: 09 Feb 2021 08:11
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3093634

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