Automatic Identification in Building Images of Biological Growths

Le résumé fourni par la source

Building rehabilitation is a reality; all rehabilitation work phases must be efficient and sustainable and promote healthy living places. Current procedures for assessing construction conditions are time-consuming, labour-intensive, and costly. They can threaten engineers’ health and safety, especially when inspecting hard-to-reach and high-altitude sites. At the initial stage, a survey of the condition of the building is conducted, which later implies the preparation of a report on the existing pathologies, intervention solutions and associated costs. This procedure involves an inspection of the site (through photographs and videos). In addition, biological growths can threaten the health of those who frequent these places. The World Health Organization states that the most important effects are the increased prevalence of respiratory symptoms, allergies, asthma, and immune system disorders. This work aims to raise awareness of this fact and contribute to the identification of an automatic form of biological growth-type defects in images of buildings. To make this possible, we need a dataset of imaging building components with and without biological growths. Subsequently, deep learning methods are applied to allow the automatic identification of this type of defect in the images, and the results are analysed. A pre-trained VGG16 model was used. The dataset was annotated and divided into groups for training, validation, and testing. The model achieved an overall accuracy of 90%. This work demonstrates the potential of using Deep Learning (DL) in the maintenance and rehabilitation of urban infrastructures, highlighting the efficiency and sustainability of these processes and the importance of adjustments to ensure the stability of AI models.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.