Application of mask RCNN for localizing and quantifying areas of energy leak in buildings using infrared images

As buildings age, energy begins to leak through various locations such as window seals, walls, subsurface cracks, and damaged areas, even in seemingly healthy structures. Most of the time, areas of energy loss remain undetected because they are not visible to the naked eye. Due to the increasing amount of energy lost through such areas and defects which has an impact on overall energy efficiency. However, infrared images (IR) can be used to detect energy leaks as well as identify subsurface damages. Infrared thermography (IRT) is a popular method for assessing the condition of buildings and infrastructures. While IRT can provide information about the location and severity of energy leaks, manually analyzing the collected data can be a cumbersome process. As a result, there is a need to automate the detection of the areas from where energy is lost. Image segmentation methods based on deep learning algorithms can effectively automate the inspection process. In this study, an approach based on a pre-trained mask region-based convolutional neural network (mask RCNN) is proposed for the first time in conjunction with IR images to localize and quantify areas of heat loss. Mask RCNN demonstrated significant accuracy in identifying the location and quantifying the size of the area of heat loss in inspected buildings with above 99% confidence.