Comparison of Automatic Recognition Models for Building Hollow Based on Infrared Thermography

To achieve efficient recognition of hollows in building external walls, this study adopts infrared thermography to construct a dedicated dataset and focuses on comparing the performance of three instance segmentation models: Mask R-CNN, YOLACT, and YOLOv8. Experimental results indicate that YOLACT is suitable for on-site rapid screening balancing accuracy and speed, YOLOv8 is more applicable to detection tasks requiring strict control over missed detections and accurate restoration of complex boundaries, while Mask R-CNN is better suited for non-real-time static image analysis. To further improve model performance, this paper introduces the position-sensitive attention (PSA) mechanism to YOLOv8 and trains the modified model. The improved model has achieved significant enhancements in various performance metrics. This study provides a reference scheme for the automatic detection of hollow defects and offers a basis for model selection under different application scenarios.