论文标题
一种深度学习的方法来识别街道视图图像中不健康的广告
A deep learning approach to identify unhealthy advertisements in street view images
论文作者
论文摘要
虽然户外广告是城镇中的常见特征，但它们可能会增强健康的社会不平等现象。在被剥夺地区的脆弱人群可能会有更大的接触，赌博，赌博和酒精广告鼓励他们的消费。了解谁被暴露并评估潜在的政策限制需要大量的手动数据收集工作。为了解决这个问题，我们开发了深度学习工作流程，以自动从街道图像中提取和分类不健康的广告。我们介绍了利物浦360 Street视图（LIV360SV）数据集，以评估我们的工作流程。该数据集包含25,349度，360度，街道级图像，该图像通过GoPro Fusion摄像机收集，录制的1月14日至18日录制。10,106张广告并归类为食物（1335），酒精（217），（217），赌博（149）和其他（8405）和其他（8405）（8405）（E.G.，CARS和BRODASS）。我们发现社会不平等的证据，其中贫困地区及其经常光经常的食物广告比例较大。我们的项目为街道视图图像的偶然分类提供了一种新颖的实施，以识别不健康的广告，提供了一种方法，通过该图像可以识别可以从更严格的广告限制政策中受益的领域，以解决社会不平等。
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.
