Image Processing for Public Health Surveillance of Tobacco Point-of-Sale Advertising: Machine Learning-Based Methodology

• Published in: Journal of medical Internet research 2021-08, Vol.23 (8), p.e24408
• Main Authors: English, Ned, Anesetti-Rothermel, Andrew, Zhao, Chang, Latterner, Andrew, Benson, Adam F, Herman, Peter, Emery, Sherry, Schneider, Jordan, Rose, Shyanika W, Patel, Minal, Schillo, Barbara A
• Format: Article
• Language: English
• Subjects: Advertising; Commerce; Humans; Machine Learning; Original Paper; Public Health Surveillance; Tobacco Products
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/24408

With a rapidly evolving tobacco retail environment, it is increasingly necessary to understand the point-of-sale (POS) advertising environment as part of tobacco surveillance and control. Advances in machine learning and image processing suggest the ability for more efficient and nuanced data capture than previously available. The study aims to use machine learning algorithms to discover the presence of tobacco advertising in photographs of tobacco POS advertising and their location in the photograph. We first collected images of the interiors of tobacco retailers in West Virginia and the District of Columbia during 2016 and 2018. The clearest photographs were selected and used to create a training and test data set. We then used a pretrained image classification network model, Inception V3, to discover the presence of tobacco logos and a unified object detection system, You Only Look Once V3, to identify logo locations. Our model was successful in identifying the presence of advertising within images, with a classification accuracy of over 75% for 8 of the 42 brands. Discovering the location of logos within a given photograph was more challenging because of the relatively small training data set, resulting in a mean average precision score of 0.72 and an intersection over union score of 0.62. Our research provides preliminary evidence for a novel methodological approach that tobacco researchers and other public health practitioners can apply in the collection and processing of data for tobacco or other POS surveillance efforts. The resulting surveillance information can inform policy adoption, implementation, and enforcement. Limitations notwithstanding, our analysis shows the promise of using machine learning as part of a suite of tools to understand the tobacco retail environment, make policy recommendations, and design public health interventions at the municipal or other jurisdictional scale.