Full-field deformation measurement of structural nodes based on panoramic camera and deep learning-based tracking method

Abstract

Full-field deformation measurement of structures generally requires the aid of complex and expensive multi-camera measurement systems. A full-field structural deformation measurement method using a single panoramic camera and deep learning-based tracking algorithm is proposed. The contributions are as follows: (1) To address the problem that existing full-field image acquisition methods rely on multi-camera systems, a full-field image acquisition method based on a single panoramic camera is proposed, in which a distortion-free planar image covering the full-field of the structure is obtained by decomposing the projection in multiple directions based on the panoramic camera imaging model and the cubic projection method. (2) To solve the problem that the nodes of structures usually contain little texture and are difficult to track robustly with existing image processing methods, an object detection network with a modified tiny feature map layer and attention mechanism is applied to extract the region of interest (ROI) of each node automatically. (3) Finally, the ROIs of the identified nodes are clustered using a perceptual hashing method and then the node coordinates are calculated using a line segment detector (LSD) and intersection fitting. The proposed method is validated on a scaled model of a stadium, and the comparison to the deformation results of total station shows that the proposed method can calculate the displacement of all node at once, and the average error between the displacement results and those of the total station is 3.7 mm, which proves the practicality of the proposed method.