Open AccessDetection of Building Defects Using CNN
Author(s) -
K. C. Santosh
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.36960
Subject(s) - ticket , convolutional neural network , computer science , unit (ring theory) , task (project management) , key (lock) , deep learning , architectural engineering , work (physics) , artificial intelligence , computer security , construction engineering , engineering , systems engineering , mechanical engineering , mathematics education , mathematics
Detection of defects together with cracks and spalls on wall surface in high-rise buildings may be a crucial task of buildings’ maintenance. purchasers area unit progressively searching for quick and effective suggests that to quickly and often survey and communicate the condition of their buildings in order that essential repairs and maintenance work will be tired a proactive and timely manner before it becomes too dangerous and big-ticket. If left unseen and untreated, these defects will considerably have an effect on the structural integrity and also the aesthetic side of buildings, timely and efficient strategies of building condition survey area unit of active want for the building house owners and maintenance agencies to switch the time- and labour-consuming approach of manual survey. so mistreatment the applying of deep learning technique of convolutional neural networks (CNN) in automating the condition assessment of buildings. the main target is to automatic detection and localisation of key defects arising from damp, patches, stains, cracks in buildings from pictures.