Machine Learning and Thermography Applied to the Detection and Classification of Cracks in Building

TL;DR

This paper explores combining infrared thermography with convolutional neural networks to improve the detection and classification of cracks in buildings, aiding in assessing their conservation state more accurately.

Contribution

It introduces a novel approach integrating thermal imaging and deep learning for crack classification, comparing different image types and fusion methods to enhance detection accuracy.

Findings

CNN model achieved high accuracy in classifying crack levels.

Thermal and fused images improved detection performance.

Input data type significantly influences model accuracy.

Abstract

Due to the environmental impacts caused by the construction industry, repurposing existing buildings and making them more energy-efficient has become a high-priority issue. However, a legitimate concern of land developers is associated with the buildings' state of conservation. For that reason, infrared thermography has been used as a powerful tool to characterize these buildings' state of conservation by detecting pathologies, such as cracks and humidity. Thermal cameras detect the radiation emitted by any material and translate it into temperature-color-coded images. Abnormal temperature changes may indicate the presence of pathologies, however, reading thermal images might not be quite simple. This research project aims to combine infrared thermography and machine learning (ML) to help stakeholders determine the viability of reusing existing buildings by identifying their pathologies and defects more efficiently and accurately. In this particular phase of this research project, we've used an image classification machine learning model of Convolutional Neural Networks (DCNN) to differentiate three levels of cracks in one particular building. The model's accuracy was compared between the MSX and thermal images acquired from two distinct thermal cameras and fused images (formed through multisource information) to test the influence of the input data and network on the detection results.