Identification and Detection in Building Images of Biological Growths – Prevent a Health Issue

Building rehabilitation is a reality, and all phases of rehabilitation work need to be efficiently sustainable and promote healthy places to live in. Current procedures for assessing construction conditions are time-consuming, laborious and expensive and pose threats to the health and safety of engineers, especially when inspecting locations that are not easy to access. In the initial step, a survey of the condition of the building is carried out, which subsequently implies the elaboration of a report on existing pathologies, intervention solutions, and associated costs. This survey involves an inspection of the site (through photographs and videos). Also, biological growth can threaten the humans inhabiting the houses. The World Health Organization states that the most important effects are increased prevalences of respiratory symptoms, allergies and asthma, as well as perturbation of the immunological system. This work aims to alert to this fact and contribute to detecting and locating biological growth (BG) defects automatically in images of the facade of buildings. To make this possible, we need a dataset of images of building components with and without biological growths. At this moment, that database doesn't exist. So, we need to construct that dataset to use deep learning models in the future. This paper also identifies the steps to do that work and presents some real cases of building façades with BG and solutions to repair those defects. The conclusions and the future works are identified.