关键词: 桥梁工程；实例分割；混凝土裂缝；YOLO11-seg；爬壁机器人

Abstract: Concrete cracks widely exist on the surfaces of bridges, roads, and buildings, affecting engineering safety. Therefore, crack damage detection of concrete structures is an important indicator for evaluating infrastructure safety. To address the problems such as low accuracy in traditional manual inspection, large parameter volumes and blurry crack details in existing crack detection models, a high-precision crack detection method for wall-climbing robots based on the YOLO-DSD network was proposed. The proposed method was based on the instance segmentation framework of the YOLO11-seg model, integrating the C3K-DWR module, the attention M-SEAM module and the Dyhead segmentation head. The YOLO-DSD algorithm reduced computational load while maintaining high-precision crack segmentation, and its accuracy surpassed that of mainstream segmentation models in the YOLO series. The research results show that compared with the baseline model, the precision of the proposed method (Precision) reaches 80.5%, with an improvement of 7.2%, and mAP50 reaches 71.6%, with an increase of 5.4%. Finally, the YOLO-DSD algorithm is deployed on a wall-climbing robot for inspection, enabling accurate extraction of crack edge information in complex backgrounds, which provides an efficient, robust and practical crack detection solution for real engineering applications.

Key words: bridge engineering; instance segmentation; concrete cracks; YOLO11-seg; wall-climbing robot