  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.
