IR Building Analysis with Extraction of Elements Using Image Segmentation and RetinaNet

Abstract

Thermography is being increasingly used in building inspection due to its capability to determine various defects, as this enables the development of improvement strategies for efficient energy consumption. In this paper, AI algorithms are combined, and new segmentation strategies are proposed to improve the accuracy of building insulation assessments. Paired visual and IR pictures taken from the same angle are used complementarily to feed different sequential neural networks employed to extract the characteristic segments of buildings. The optical images contain the information required to identify and separate objects, such as windows, doors, and walls. The IR pictures contain the information required for the insulation assessment. This enables an automated analysis of a large number of objects within the same assessment with respect to the proper viewing angle and resolution. Variations in measured temperatures for segmented regions are estimated by referring to their representations in the IR frames, which allows for general conclusions concerning insulation state to be drawn, and by using a trained neural network, heat losses are localized in the frames. The output levels of consecutive IR frames are compared to determine the effects on IR object representation due to different recording aspects.