Multi-stage detection of warped ceiling panel using ensemble vision models for automated localization and quantification

Suspended ceiling systems constitute a pivotal non-structural component in buildings, and the warping of panels not only compromises the seismic performance but also affects the functional integrity. This paper proposes a novel multi-stage warped panel detection (MWPD) method to automatically locate warped panels from two-dimensional images and quantify their deformation. First, the Deep Hough Transform (DHT) is employed to localize the runner line, after that, each detected line is expanded to a rectangular strip. Then ResNet18 classifies the strips as warped or intact. Those classified as warped will undergo Gabor and horizontal Sobel filters successively to highlight the curved edge. Subsequently, the Generalized Hough Transform (GHT) is used to locate pixel points on the curve, and fitting these points yields the pixel-level radius of curvature. Leveraging known orthogonal relationships and geometric dimensions of runners, pixel quantification is converted into physical maximum deflection. The experiments include two aspects: the first is conducted on a validation dataset to verify the localization stability, and the second is carried out on-site for quantification validation. Results demonstrate that the proposed MWPD method effectively localizes the warped panel, achieving an accuracy of 92.2% on the validation dataset. Additionally, the quantitative test has achieved an accuracy of approximately 85%.