SANG W G, ZHU Y Z, SUN J F,et al.Segment extraction method for metro tunnel linings based on the Scaramuzza distortion correction model[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(4):22-29.

doi:10.16186/j.cnki.1673-9787.2026020009

Received:2026/02/10

Revised:2026/04/24

Published:2026/06/17

Segment extraction method for metro tunnel linings based on the Scaramuzza distortion correction model

Sang Wengang, Zhu Youzhi, Sun Junfeng, Yin Yibo

School of Surveying and Geo-Informatics， Shandong Jianzhu University， Ji’nan 250101， Shandong， China

Abstract: Objectives To overcome the limitations of high cost and complex data processing in traditional tunnel segment monitoring technologies， as well as the influence of wide-angle lens distortion of panoramic cameras on the reliability of segment feature extraction， this study investigates a metro tunnel segment extraction method based on the Scaramuzza distortion correction model using panoramic cameras to meet the requirements of routine and refined monitoring of metro tunnel segments and ensure tunnel operation safety.  Methods Focusing on high-precision segment monitoring and accurate extraction of geometric features， an imaging geometric mapping relationship is established based on the Scaramuzza model， and homogeneous constraint equations are constructed to estimate the intrinsic and extrinsic camera parameters. The optimal polynomial order is determined using a stepwise order-increasing verification method to optimize calibration performance， with reprojection error adopted as the core evaluation index. After lens distortion correction， a 3D tunnel model is constructed and ring-wise unfolding of tunnel segments is achieved. The effectiveness of the proposed method is validated from three aspects： linear feature restoration， geometric shape recovery， and accuracy evaluation.  Results Experimental results show that the average reprojection error of calibration based on the Scaramuzza model is reduced to 0.33 pixels， representing an improvement of approximately 37.9% compared with the Fisheye model. After distortion correction， the maximum perimeter deviation and maximum angular deviation of the calibration checkerboard are 1.93 mm and 0.04°， respectively. In addition， the average positional uncertainty of connection points in the 3D model is reduced by 33.24%， indicating improved data quality. Conclusions The proposed method features non-contact measurement， full-coverage detection， and strong robustness， while achieving a balance among monitoring accuracy， efficiency， and cost. It provides a feasible approach for refined monitoring of metro tunnel segments and similar application scenarios and demonstrates practical engineering value.

Key words:metro tunnel;segment monitoring;panoramic camera;distortion correction;3D model