Multimodal geometric AutoEncoder (MGAE) for rail fasteners tightness evaluation with point clouds & monocular depth fusion

作     者：Qiu, Shi Zaheer, Qasim Shah, S. Muhammad Ahmed Hassan Shah, Syed Faizan Hussain Ehsan, Haleema Atta, Zunaira Ai, Chengbo Wang, Jin Wang, Weidong Peng, Jun 

作者机构：Cent South Univ Sch Civil Engn Changsha 410075 Peoples R China Cent South Univ MOE Key Lab Engn Struct Heavy Haul Railway Changsha 410075 Peoples R China Cent South Univ Ctr Railway Infrastruct Smart Monitoring & Managem Changsha 410075 Peoples R China Univ Massachusetts Dept Civil & Environm Engn Amherst MA 01003 USA 

出 版 物：《MEASUREMENT》 (Meas J Int Meas Confed)

年 卷 期：2025年第244卷

核心收录：

学科分类：08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

基　　金：National Natural Science Foun-dation of China 

主　　题：Rail fasteners Fastener tightness Rail infrastructure Autoencoder Multimodal approach 

摘      要：Accurate detection and estimation of railway fastener tightness are vital for rail infrastructure safety and reliability. Traditional methods depend on manual annotation tools like Label Me, which are error-prone, laborintensive, and costly. Additionally, monocular depth estimation and instance segmentation involve complex computations that challenge real-time implementation, particularly on resource-constrained platforms. This study introduces a novel three-phase solution using the Multimodal Geometric Autoencoder (MGAE) for fastener tightness detection, integrating point clouds with monocular-depth-guided multimodal data. Our approach utilizes a hybrid autoencoder for high-quality feature extraction, enabling precise tightness estimation. Employing unsupervised learning, MGAE eliminates the need for labeled data, thus reducing labor and costs. The framework integrates point clouds, mesh, monocular depth, and 2D images, with various fusion blocks enhancing feature extraction accuracy and computational efficiency. Post-feature extraction, classical techniques such as isolation forest, stress-strain, and elastic potential energy methods assess fastener tightness.