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.

翻译：苏格兰的建筑节能改造与热成像调查（TS）公司与社会住房供应商合作，以应对燃料贫困问题。他们使用地面红外（IR）相机热成像调查（GIRTS）收集热图像，以识别因保温不良导致的热损失源。然而，这一识别过程劳动密集且耗时，需要大量数据处理。为实现自动化，亟需一种人工智能驱动的方法。为此，本研究提出了一种基于深度学习（DL）的分割框架，采用掩码区域提议卷积神经网络（Mask RCNN），以验证其在热图像中的应用可行性。该框架的目标是自动识别并裁剪由保温薄弱引起的热损失源，同时消除图像中的干扰物体。通过这种方式，它减少了劳动密集型任务，并提供了一种自动化、一致且可靠的解决方案。为验证该框架，与工业热成像合作伙伴共同收集了约2500张热图像。随后，在专家协助下，精心挑选了1800张代表性图像，并对目标物体（TO）进行标注以形成最终数据集。之后，采用迁移学习策略训练数据集，逐步增加训练数据量并微调预训练的基线Mask RCNN。最终，微调后的模型在分割目标物体时达到了77.2%的平均精度均值（mAP），证明了所提框架在准确量化苏格兰家庭能源损失方面的巨大潜力。