面向隧道检测的偏心线阵影像校正方法研究

Study on image correction of misaligned line-scan imaging for tunnel disease detection

  • 摘要: 隧道表观病害的高效检测对降低养护成本与预防事故意义重大,线阵相机因分辨率高、可连续扫描,已成为隧道影像获取的重要工具,但受空间限制,相机多采用偏心安装,易导致影像产生复合畸变,影响病害识别精度。现有校正方法或依赖标定物与精确位姿,或仅针对单一畸变类型,难以满足复杂环境需求。为此,提出一种递进式影像校正方法,通过建立基于隧道几何先验的偏心线阵相机成像模型,揭示位姿偏移引起复合畸变的机理,依次进行方向、倾斜与正射校正,以逐级消除畸变。该方法利用隧道结构特征实现自动化校正,无需精确位姿。工程试验表明,校正后标定板格网量测平均相对误差由43.5%降至8.6%,偏心安装引起的复合畸变得以有效消除。

     

    Abstract: Efficient detection of tunnel surface defects is critical for reducing maintenance costs and preventing accidents. Line-scan cameras have emerged a pivotal tool for tunnel image acquisition, owing to their high resolution and continuous scanning capability. However, spatial constraints often necessitate misaligned installation of the cameras, which may introduce compound image distortions and affect defect recognition accuracy. Existing correction methods typically rely on calibration targets and accurate camera poses or address only a single type of distortion, limiting their applicability in complex tunnel environments. To address these limitations, a misaligned line-scan imaging model based on tunnel geometric priors was established to characterize the compound distortions caused by camera pose deviations. A progressive correction method was then developed, in which orientation correction, tilt correction, and orthorectification were performed in sequence to rectify the distortions stepwise. Tunnel structural features were used for automatic correction without requiring accurate camera poses. Engineering tests showed that the mean relative error of calibration-grid measurements decreased from 43.5% to 8.6%, demonstrating that the compound distortions caused by misaligned installation were effectively corrected.

     

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