ENSTRECT: A Stage-based Approach to 2.5D Structural Damage Detection
It addresses damage assessment for civil structures, offering a stage-based method with specific performance gains.
The paper tackles 2.5D structural damage detection by localizing and segmenting surface damages like cracks, spalling, and corrosion from images and point clouds, achieving IoUs over 90% for cracks and AP50 up to 56% for corrosion.
To effectively assess structural damage, it is essential to localize the instances of damage in the physical world of a civil structure. ENSTRECT is a stage-based approach designed to accomplish 2.5D structural damage detection. The method requires an image collection, the relative orientation, and a point cloud. Using these inputs, surface damages are segmented at the image level and then mapped into the point cloud space, resulting in a segmented point cloud. To enable further quantitative analyses, the segmented point cloud is transformed into measurable damage instances: cracks are extracted by contracting the clustered point cloud into a corresponding medial axis. For areal damages, such as spalling and corrosion, a procedure is proposed to compute the bounding polygon based on PCA and alpha shapes. With a localization tolerance of 4cm, ENSTRECT can achieve IoUs of over 90% for cracks, 82% for corrosion, and 41% for spalling. Detection at the instance level yields an AP50 of about 45% (cracks, spalling) and 56% (corrosion).