Intelligent recognition of defects in high‐speed railway slab track with limited dataset

作者：Xiaopei Cai, Xueyang Tang, Shuo Pan, Yi Wang, Hai Yan, Yuheng Ren, Ning Chen, Yue Hou · 发表于：Computer-Aided Civil and Infrastructure Engineering · 年份：2023 · DOI：10.1111/mice.13109 · 被引用次数：54 · 研究领域：Infrastructure Maintenance and Monitoring、Railway Engineering and Dynamics、Non-Destructive Testing Techniques

During the regular service life of high-speed railway (HSR), there might be serious defects in the concrete slabs of the infrastructure systems, which may further significantly affect public transportation safety. To address these serious issues and fulfill the regular functions of HSR, the traditional methods for railway engineers involve carrying out regular on-site inspections manually or by semi-automatic inspection vehicles, and conducting timely corresponding repairing approaches and maintenance, where these methods are time-consuming and dangerous. In recent years, machine learning methods have been widely applied to the intelligent and automatic detection of severe defects in HSR. Currently, one of the most serious problems is the lack of sufficient high-quality data for model training, resulting in low recognition accuracy in HSR defects. To solve this problem, this paper proposed an intelligent recognition of defects in concrete slabs of HSR based on a few-shot learning model, that is, an artificial intelligence model based on limited data size, which recognizes three service conditions of concrete slabs in HSR: cracks, track board gaps, and unbroken state. Lightweight few-shot learning models specifically designed for HSR detection were proposed. Experiments were conducted to compare the performances of different lightweight-designed models, including accuracy, parameter quantity, and testing time. Results showed that the optimum model can fast and satisfactorily r...