Comparison of Automatic Recognition Models for Building Hollow Based on Infrared Thermography
Year: 2026
Type: article
Abstract: 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.
Source: Applied Sciences
DOI: 10.3390/app16063075
Institution: Fuzhou University
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7140127964