Perez, H.; Tah, J.H.M.; Mosavi, A. Deep Learning for Detecting Building Defects Using Convolutional Neural Networks. Sensors2019, 19, 3556.
Perez, H.; Tah, J.H.M.; Mosavi, A. Deep Learning for Detecting Building Defects Using Convolutional Neural Networks. Sensors 2019, 19, 3556.
Perez, H.; Tah, J.H.M.; Mosavi, A. Deep Learning for Detecting Building Defects Using Convolutional Neural Networks. Sensors2019, 19, 3556.
Perez, H.; Tah, J.H.M.; Mosavi, A. Deep Learning for Detecting Building Defects Using Convolutional Neural Networks. Sensors 2019, 19, 3556.
Abstract
Clients are increasingly looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential repairs and maintenance work can be done in a proactive and timely manner before it becomes too dangerous and expensive. Traditional methods for this type of work commonly comprise of engaging building surveyors to undertake a condition assessment which involves a lengthy site inspection to produce a systematic recording of the physical condition of the building elements, including cost estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building. Current asset condition assessment procedures are extensively time consuming, laborious, and expensive and pose health and safety threats to surveyors, particularly at height and roof levels which are difficult to access. We propose a method for automated detection and localisation of key building defects from images using deep learning and convolution neural networks. The proposed model is based on a pre-trained VGG-16 classifier with Class Activation Mapping (CAM) for object localisation. The model has proven to be robust and able to accurately detect and localise mould growth, stains, and paint deterioration defects arising from dampness in buildings. The approach is being developed with potentials to scale-up to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.
Keywords
deep learning; convolutional neural networks (CNN); transfer learning; class activation mapping (CAM); building defects; structural-health monitoring
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received:
4 May 2022
Commenter:
sugandapu Hemalatha
The commenter has declared there is no conflict of interests.
Comment:
Hi team, I have gone through the entire article and it is too clear to understand but i am requesting you to please send me the code to made it understand in reality and it would be helpful to me for my further research..
Commenter: sugandapu Hemalatha
The commenter has declared there is no conflict of interests.
Please send me the reply.