Bridge inspection using image–point cloud fusion with image filtering, damage detection and 3D registration

Complex image backgrounds often compromise the reliability of damage detection. In bridge inspection, a further challenge lies in accurately recording and localizing the detected damage onto a 3D model. Based on image and point cloud data (PCD) fusion, this paper proposes a five-step methodology for detecting bridge damage and registering it on a 3D model. High-quality images and PCD files are simultaneously collected using a LiDAR 3D camera with their relationships clearly recorded. The complete bridge PCD is segmented and subsequently utilized to select images containing needed components and filter out the background via 3D-to-2D projection. Damage is detected from background-filtered images and then registered on the bridge PCD through 2D-to-3D projection. An experiment conducted on an actual bridge validated the feasibility of the proposed framework, confirming that the methodology not only produces clear and intuitive 3D visualizations of damage but also effectively supports detailed inspection and maintenance tasks. • A form of image and point cloud data fusion is proposed for bridge inspection. • Accurate 2D–3D bidirectional projections are achieved due to fixed parameters. • Damage detection performance is improved using background-filtered images. • The results support intuitive 3D visualization and advanced inspection tasks. • The system is straightforward to operate and reproduce since most parts rely on standard algorithms.