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

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, which may encourage their consumption. Understand...

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Autores principales:	Gregory Palmer, Mark Green, Emma Boyland, Yales Stefano Rios Vasconcelos, Rahul Savani, Alex Singleton
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d117b04462664ea4af4b0794b6d30ede
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id	oai:doaj.org-article:d117b04462664ea4af4b0794b6d30ede
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spelling	oai:doaj.org-article:d117b04462664ea4af4b0794b6d30ede2021-12-02T15:54:01ZA deep learning approach to identify unhealthy advertisements in street view images10.1038/s41598-021-84572-42045-2322https://doaj.org/article/d117b04462664ea4af4b0794b6d30ede2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84572-4https://doaj.org/toc/2045-2322Abstract 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, which may encourage 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 $${360}^{\circ }$$ 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). 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.Gregory PalmerMark GreenEmma BoylandYales Stefano Rios VasconcelosRahul SavaniAlex SingletonNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Gregory Palmer Mark Green Emma Boyland Yales Stefano Rios Vasconcelos Rahul Savani Alex Singleton A deep learning approach to identify unhealthy advertisements in street view images
description	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, which may encourage 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 $${360}^{\circ }$$ 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). 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.
format	article
author	Gregory Palmer Mark Green Emma Boyland Yales Stefano Rios Vasconcelos Rahul Savani Alex Singleton
author_facet	Gregory Palmer Mark Green Emma Boyland Yales Stefano Rios Vasconcelos Rahul Savani Alex Singleton
author_sort	Gregory Palmer
title	A deep learning approach to identify unhealthy advertisements in street view images
title_short	A deep learning approach to identify unhealthy advertisements in street view images
title_full	A deep learning approach to identify unhealthy advertisements in street view images
title_fullStr	A deep learning approach to identify unhealthy advertisements in street view images
title_full_unstemmed	A deep learning approach to identify unhealthy advertisements in street view images
title_sort	deep learning approach to identify unhealthy advertisements in street view images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/d117b04462664ea4af4b0794b6d30ede
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A deep learning approach to identify unhealthy advertisements in street view images

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