Abstract:
To ensure sustainability of buildings, detection of building defects is crucial.
Conventional practices of defects detection from building inspection data are mostly
manual and error prone. With the advancements in computer vision, imaging technology
and machine learning-based tools have been developed for real-time, accurate and
efficient defects detection. Deep learning (DL), which is a branch of ML is more robust
in automatically retrieving elements’ semantics to detect building defects. Although DL
algorithms are robust in object detection, the computational complexities and
configurations of these models are high. Therefore, this study presents a process of
developing a computationally inexpensive and less complicated DL model using transfer
learning and Google Colab virtual machine to improve automation in building defects
detection. Cracks is one of the major building defects that constraint the safety and
durability of buildings thus hindering building sustainability. Building cracks images
were sourced from the Internet to train the model, which was built upon You Only Look
Once (YOLO) DL algorithm. To test the DL model, inspection images of five (05)
buildings collected by the Facilities Management department of a University in Sydney
city were used. The DL model developed using this process offers a monitoring tool to
ensure the sustainability of buildings with its’ ability of detecting cracks from building
inspection data in real time accurately and efficiently. Although the current model is
built to detect cracks, this process can be employed to automated detection of any
building defect upon providing the training images of defects.
