Engineering-oriented automated segmentation and quantitative analysis of building structural cracks: A multi-mechanism optimized YOLOv11-seg approach

During the long-term service of existing buildings, the initiation and propagation of structural cracks are critical indicators for evaluating building safety and durability. Computer vision techniques have achieved substantial progress in crack detection for bridges and road pavements, and considerable research has also been carried out on building crack identification. However, due to the complex morphology, diverse surface textures, and complex background environments of building cracks, most existing models trained for traffic infrastructure cannot be directly well adapted to practical building scenarios. In addition, existing public crack datasets are mostly oriented to road and bridge engineering, lacking targeted samples and refined feature descriptions for building structural cracks, which makes it difficult to support high-precision segmentation and geometric parameter quantification in building engineering. To address these application gaps, this study firstly constructs a dedicated building crack segmentation dataset BCrack containing 450 multi-scene crack images. On this basis, an improved YOLOv11-seg model (YOLO-MDAC) with multi-mechanism optimization is proposed. The mAP50 is increased from 80.2% to 83.4%, effectively improving the accuracy of crack edge segmentation. Furthermore, the proposed method is integrated with a quantitative calculation module for crack length and width. Field test results show that the measurement errors can be controlled within 5%. This research provides an efficient and reliable technical means for structural damage assessment of existing buildings, and presents good engineering application and promotion value.