Quantitative identification of debonding defects in building façades based on UAV-thermography using a two-stage network integrating dual attention mechanism

The debonding defects in building facades pose a serious threat to the safety of residents. In this paper, a twostage quantitative network for debonding defect identification quickly and accurately based on deep learning is proposed. Firstly, the rotor UAV equipped with an infrared thermal imager is applied as the working platform to detect the debonding defects in building facades. Then, the target detection network combining dual attention mechanism, improved activation function, and bilinear interpolation has been proposed to accurately recognize infrared images and suppress background interference. Further, the semantic segmentation network with channel attention mechanism has been proposed to obtain more accurate defect area boundaries and shape information. Finally, compared with the classical deep learning networks, the results show that the improved algorithm can accurately identify the type and shape information of debonding defects.