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. This paper aims at evaluating the application of convolutional neural networks (CNN) towards an automated detection and localisation of key building defects, e.g., mould, deterioration, and stain, from images. The proposed model is based on pre-trained CNN classifier of VGG-16 (later compaired with ResNet-50, and Inception models), with class activation mapping (CAM) for object localisation. The challenges and limitations of the model in real-life applications have been identified. The proposed model has proven to be robust and able to accurately detect and localise building defects. The approach is being developed with the potential to scale-up and further advance to support automated detection of defects and deterioration of buildings in real-time using mobile devices and drones.
Highlights
Clients with multiple assets are increasingly requiring intimate knowledge of the condition of each of their operational assets to enable them to effectively manage their portfolio and improve business performance
We propose a deep learning-based detection and localisation model using transfer learning utilising the VGG-16 model for feature extraction and classification
ForModel our model, we applied a fine-tuning transfer learning to a VGG-16 network pre-trained on ImageNet; a huge dataset of images containingtransfer more than 14 million annotated images pre-trained and more than
Summary
Clients with multiple assets are increasingly requiring intimate knowledge of the condition of each of their operational assets to enable them to effectively manage their portfolio and improve business performance. Clients are looking for fast and effective means to quickly and frequently survey and communicate the condition of their buildings so that essential maintenance and repairs can be done in a proactive and timely manner before it becomes too dangerous and expensive [1,2,3,4]. The data collected are analysed to produce a report that includes a summary of the condition of the building and its elements [8] This is used to produce estimates of immediate and projected long-term costs of renewal, repair and maintenance of the building [9,10].
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