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

Gregory Palmer,Mark Green,Emma Boyland,Rahul Savani,Yales Stefano Rios Vasconcelos,Alex Singleton

doi:10.1038/s41598-021-84572-4

Gregory Palmer, Mark Green + Show 4 more

Open Access

https://doi.org/10.1038/s41598-021-84572-4

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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, 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 } 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.

Highlights

The impact of unhealthy advertisements.The Commercial Determinants of Health (CDoH), defined here as the processes where private organisations prioritise profit over public health, are powerful drivers of trends in non-communicable diseases and health inequalities[14,15]
The aim of our study is to develop a deep learning workflow to automatically extract and classify unhealthy advertisements from street view images
We find that the pre-trained network achieves a mean intersection over union score of 0.397 for the category billboard on the Mappilary Vistas validation set

Summary

Introduction

The Commercial Determinants of Health (CDoH), defined here as the processes where private organisations prioritise profit over public health, are powerful drivers of trends in non-communicable diseases and health inequalities[14,15]. Organisations may encourage the consumption of unhealthy products through marketing and advertisements campaigns across multiple platforms. There is a growing concern among public health officials regarding the number of advertisements for risky products e.g., alcohol, gambling, unhealthy food and beverages[16,17]. Numerous studies conducted around the world indicate that exposure to unhealthy energy–dense, nutrition-poor food and beverage advertisements can promote unhealthy eating habits[18–24]. The marketing of products that are high in fat, sugar and salt to children is concerning, as it increases the potential for diet–related diseases later in life[21]. Exposing adolescents to alcohol advertisements has been found to encourage early usage, and can lead to an increase in consumption[25], while gambling advertisements can trigger an impulse to increase activities, in particular in individuals who want to either quit or gamble less frequently[26]

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