Utilizing Deep Learning-Based Fusion of Laser Point Cloud Data and Imagery for Digital Measurement in Steel Truss Member Applications

In efforts to refine the digital measurement accuracy of steel truss bridge rods, a novel methodology was proposed, integrating laser point cloud technology with advanced image processing. Point cloud data, derived from stationary and handheld scanners, was meticulously fused with image datasets to produce precise rod models. Specialised algorithms tailored for point cloud data segmentation, edge detection, and geometric feature extraction were employed to derive accurate geometric attributes of the rods. Furthermore, deep learning techniques were harnessed for image segmentation and feature extraction, predicting potential deformations and delineating damage areas, significantly enhancing the accuracy of feature recognition. Through finite element analysis, errors introduced from non-fixed deformations during the scanning phase were meticulously rectified. Validations suggest that this innovative digital measurement approach, blending laser point cloud and sophisticated image processing, notably outperforms conventional methodologies in terms of precision and efficiency, offering promising avenues for subsequent research and applications in the realm of digital measurements.