  Retrofitting and thermographic survey (TS) companies in Scotland collaborate
with social housing providers to tackle fuel poverty. They employ ground-level
infrared (IR) camera-based-TSs (GIRTSs) for collecting thermal images to
identi-fy the heat loss sources resulting from poor insulation. However, this
identifica-tion process is labor-intensive and time-consuming, necessitating
extensive data processing. To automate this, an AI-driven approach is
necessary. Therefore, this study proposes a deep learning (DL)-based
segmentation framework using the Mask Region Proposal Convolutional Neural
Network (Mask RCNN) to validate its applicability to these thermal images. The
objective of the framework is to au-tomatically identify, and crop heat loss
sources caused by weak insulation, while also eliminating obstructive objects
present in those images. By doing so, it min-imizes labor-intensive tasks and
provides an automated, consistent, and reliable solution. To validate the
proposed framework, approximately 2500 thermal imag-es were collected in
collaboration with industrial TS partner. Then, 1800 repre-sentative images
were carefully selected with the assistance of experts and anno-tated to
highlight the target objects (TO) to form the final dataset. Subsequently, a
transfer learning strategy was employed to train the dataset, progressively
aug-menting the training data volume and fine-tuning the pre-trained baseline
Mask RCNN. As a result, the final fine-tuned model achieved a mean average
precision (mAP) score of 77.2% for segmenting the TO, demonstrating the
significant po-tential of proposed framework in accurately quantifying energy
loss in Scottish homes.
