Preprint Article Version 1 This version is not peer-reviewed

Deep Learning for Detecting Building Defects Using Convolutional Neural Networks

Version 1 : Received: 4 August 2019 / Approved: 6 August 2019 / Online: 6 August 2019 (04:18:29 CEST)

A peer-reviewed article of this Preprint also exists.

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. Sensors 2019, 19, 3556.

Journal reference: Sensors 2019, 19, 3556
DOI: 10.3390/s19163556

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.

Subject Areas

deep learning; convolutional neural networks (CNN); transfer learning; class activation mapping (CAM); building defects; structural-health monitoring

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