Abstract

In high-pier bridge construction, the quality and accuracy of embedded rebar placement are critical to ensuring structural safety and durability. However, conventional manual inspection methods are inefficient, subjective and pose significant safety risks in high-altitude operations. These methods are unable to comprehensively inspect all pier columns on a daily basis, and frequently result in delays in acceptance that necessitate rework. In order to address these challenges, the current study proposes a smart vision-based inspection framework for the automatic and high-precision quality assessment of rebar under long-distance imaging conditions. This approach allows quality inspectors to remotely predict and evaluate the embedment quality of rebars from a safe distance. Notably, this work introduces a novel dual-source coordinate fusion mechanism that integrates improved instance segmentation with corner detection for global-to-local precision enhancement, representing an original contribution to rebar placement inspection in complex high-pier scenarios. The framework integrates an improved YOLOv8-CD segmentation model and a corner detection algorithm through a dual-source coordinate fusion mechanism, achieving an integration of global rebar detection and local feature enhancement. The YOLOv8-CD model, when optimised, features the Convolutional Block Attention Module (CBAM) integrated into the backbone, with the objective of enhancing recognition accuracy for small targets. Additionally, a Dilation-Wise Residual (DWR) module has been inserted before the neck C2f layer for the purpose of strengthening multi-scale feature extraction. The process of perspective correction and pixel-to-actual-length conversion coefficienting is performed in order to achieve a millimetre-level measurement of the rebar spacing and diameter. Empirical validation through real high-pier construction scenes demonstrates that the proposed framework attains a detection accuracy of 98.82%, surpassing conventional YOLO-based and single-source methodologies. The experimental results demonstrate that this framework is able to detect objects at longer distances, and to maintain its performance when the target is at a greater distance than that which was used for training. The proposed approach is expected to provide an efficient, safe, and quantitative solution for intelligent bridge construction quality monitoring, offering valuable insights for the future development of smart construction and structural health inspection systems.

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