The quality of concrete consolidation is critical to the structural safety of dams. However, current vibration machines lack reliable information regarding the distribution map of concrete vibration states in a pouring block. This informational deficiency often causes machinery to repeatedly traverse previously vibrated regions, which compromises the internal structural integrity of the concrete. Accurately recognizing these vibrated regions is challenging because vibration-induced particle rearrangement and concrete flow-ability lead to high inter-class similarity, minimal color variance, and blurred boundaries in practical construction environments. To address this issue, this study proposes a recognition method utilizing a Transformer architecture with masked attention. This design enhances feature representation, effectively focusing on target regions and ambiguous boundaries while suppressing background noise. Experimental results demonstrate the robustness of the proposed method, achieving a mean Average Precision of approximately 0.9. This performance outperforms existing benchmark models, exceeding YOLACT, YOLO11, CondInst, and SOLOv2 by roughly 97%, 53%, 17%, and 12%, respectively. Practically, embedding concrete state distribution information into machinery provides visual guidance for path planning, enabling a lean control from data acquisition to physical execution. Future research will integrate these findings with construction equipment to enable autonomous operation and decision-making for intelligent quality control.
Lei et al. (2026) studied this question.