Underwater pipelines are highly susceptible to corrosion, which not only shorten their service life but also pose significant safety risks. Compared with manual inspection, the intelligent real-time imaging system for underwater pipeline detection has become a more reliable and practical solution. Among various underwater imaging techniques, structured light 3D imaging can restore the sufficient spatial detail for precise defect characterization. Therefore, this paper develops a multi-mode underwater structured light 3D imaging system for pipeline detection (UW-SLD system) based on multi-source information fusion. First, a rapid distortion correction (FDC) method is employed for efficient underwater image rectification. To overcome the challenges of extrinsic calibration among underwater sensors, a factor graph-based parameter optimization method is proposed to estimate the transformation matrix between the structured light and acoustic sensors. Furthermore, a multi-mode 3D imaging strategy is introduced to adapt to the geometric variability of underwater pipelines. Given the presence of numerous disturbances in underwater environments, a multi-source information fusion strategy and an adaptive extended Kalman filter (AEKF) are designed to ensure stable pose estimation and high-accuracy measurements. In particular, an edge detection-based ICP (ED-ICP) algorithm is proposed. This algorithm integrates pipeline edge detection network with enhanced point cloud registration to achieve robust and high-fidelity reconstruction of defect structures even under variable motion conditions. Extensive experiments are conducted under different operation modes, velocities, and depths. The results demonstrate that the developed system achieves superior accuracy, adaptability and robustness, providing a solid foundation for autonomous underwater pipeline detection.

翻译：水下管道极易受到腐蚀，这不仅缩短其使用寿命，还带来重大的安全风险。与人工检测相比，用于水下管道检测的智能实时成像系统已成为更可靠且实用的解决方案。在各种水下成像技术中，结构光三维成像能够还原足够的空间细节以实现精确的缺陷表征。因此，本文开发了一种基于多源信息融合的、用于管道检测的多模态水下结构光三维成像系统（UW-SLD系统）。首先，采用一种快速畸变校正（FDC）方法进行高效的水下图像校正。为克服水下传感器间外参标定的挑战，提出了一种基于因子图的参数优化方法来估计结构光与声学传感器之间的变换矩阵。此外，引入了一种多模态三维成像策略以适应水下管道的几何变异性。鉴于水下环境中存在大量干扰，设计了多源信息融合策略和自适应扩展卡尔曼滤波器（AEKF），以确保稳定的位姿估计和高精度测量。特别地，提出了一种基于边缘检测的ICP（ED-ICP）算法。该算法将管道边缘检测网络与增强的点云配准相结合，即使在变化的运动条件下也能实现缺陷结构的鲁棒且高保真重建。在不同操作模式、速度和深度下进行了大量实验。结果表明，所开发的系统具有卓越的精度、适应性和鲁棒性，为自主水下管道检测奠定了坚实基础。