Intelligent Detection Method of Defects in High-Rise Building Facades Using Infrared Thermography

High-rise building facades are prone to defects due to prolonged exposure to complex environments. Infrared detection, as a commonly employed method for facade defect inspection, often results in low accuracy owing to abundant interferences and blurred defect boundaries. In this work, an intelligent defect detection method for high-rise building facades is proposed. In the first stage of the proposed method, a segmentation model based on DeepLabV3+ is proposed to remove interferences in infrared images using masks. The model incorporates a Post-Decoder Dual-Branch Boundary Refinement Module, which is subdivided into a boundary feature optimization branch and a boundary-guided attention branch. Sub-pixel-level contour refinement and boundary-adaptive weighting are hence achieved to mitigate edge blurring induced by thermal diffusion and to enhance the perception of slender cracks and cavity edges. A triple constraint mechanism is also introduced, combining cross-entropy, multi-scale Dice, and boundary-aware losses to address class imbalance and enhance segmentation performance for small targets. Furthermore, superpixel linear iterative clustering (SLIC) is utilized to enforce regional consistency, hence improving the smoothness and robustness of predictions. In the second stage of the proposed method, a defect detection model based on YOLOV11 is proposed to process masked infrared images for detecting hollow, seepage, cracks and detachment. This work validates the proposed method using 180 infrared images collected via unmanned aerial vehicles. The experimental results demonstrate that the proposed method achieves a detection precision of 89.7%, an mAP@0.5 of 87.9%, and a 57.8 mAP@50-95. surpassing other algorithms and confirming its effectiveness and superiority.