Automated cooling tower detection through deep learning for Legionnaires' disease outbreak investigations: a model development and validation study.

ABSTRACT:

Background

Cooling towers containing Legionella spp are a high-risk source of Legionnaires' disease outbreaks. Manually locating cooling towers from aerial imagery during outbreak investigations requires expertise, is labour intensive, and can be prone to errors. We aimed to train a deep learning computer vision model to automatically detect cooling towers that are aerially visible.

Methods

Between Jan 1 and 31, 2021, we extracted satellite view images of Philadelphia (PN, USA) and New York state (NY, USA) from Google Maps and annotated cooling towers to create training datasets. We augmented training data with synthetic data and model-assisted labelling of additional cities. Using 2051 images containing 7292 cooling towers, we trained a two-stage model using YOLOv5, a model

SUBMITTER: Wong KK 

PROVIDER: S-EPMC12038768 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature