当前位置: X-MOL 学术Autom. Constr. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Tunnel crack assessment using simultaneous localization and mapping (SLAM) and deep learning segmentation
Automation in Construction ( IF 9.6 ) Pub Date : 2025-01-18 , DOI: 10.1016/j.autcon.2025.105977
Huitong Xu, Meng Wang, Cheng Liu, Yongchao Guo, Zihan Gao, Changqing Xie

Artificial intelligence algorithms and multi-sensor technologies are advancing tunnel crack detection. However, image-based detection methods fail to account for tunnel section curvature, limiting their ability to represent the spatial geometry of cracks. To address these problems, this paper presents a tunnel crack assessment method combining simultaneous localization and mapping (SLAM) with deep learning-based segmentation. The SLAM algorithm reconstructs the tunnel point cloud map, and a two-dimensional (2D) convex hull point cloud unfolding with a cloth simulation filter (CSF) algorithm is applied for denoising. A deep learning segmentation model is used to segment the tunnel cracks. The cracks are projected into a three-dimensional (3D) point cloud map, and the crack length and spatial location are calculated. Field tests demonstrate that the method reduces tunnel reconstruction time to 27 s (a 99 % time saving), with a maximum radius error of 0.03 m and accurate 3D crack projections.

中文翻译:


使用同步定位和地图构建 (SLAM) 和深度学习分割进行隧道裂缝评估



人工智能算法和多传感器技术正在推进隧道裂缝检测。然而,基于图像的检测方法无法考虑隧道截面曲率,限制了它们表示裂缝空间几何形状的能力。为了解决这些问题,本文提出了一种将同步定位和地图构建 (SLAM) 与基于深度学习的分割相结合的隧道裂缝评估方法。SLAM 算法对隧道点云地图进行重构,并采用布料模拟滤波器 (CSF) 算法展开的二维 (2D) 凸包点云进行去噪。深度学习分割模型用于分割隧道裂缝。将裂缝投影到三维 (3D) 点云地图中,并计算裂缝长度和空间位置。现场测试表明,该方法将隧道重建时间缩短至 27 秒(节省了 99% 的时间),最大半径误差为 0.03 m,并实现了精确的 3D 裂缝投影。
更新日期:2025-01-18
down
wechat
bug