Research on the Application of Time-Frequency Characteristics of GPR in Railway Mud Pumping Intelligent Detection.
Saved in:
| Title: | Research on the Application of Time-Frequency Characteristics of GPR in Railway Mud Pumping Intelligent Detection. |
|---|---|
| Authors: | Shi, Wenxing1 (AUTHOR), Wang, Shilei2 (AUTHOR) wangshilei@rails.cn, Yang, Feng1,3 (AUTHOR), Zhang, Chi1,2 (AUTHOR), Li, Fanruo1,2 (AUTHOR), Peng, Suping3 (AUTHOR) |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1393. 28p. |
| Subjects: | Time-frequency analysis, Ground penetrating radar, Railroads, Nondestructive testing, Deep learning |
| Abstract: | Highlights: What are the main findings? The proposed YOLO-DGW framework, integrating time-frequency collaborative feature modeling, small wavelet-based frequency modulation, and A-CIoU loss, significantly improves detection of weak, irregular mud pumping anomalies in railway GPR B-scan, achieving higher Precision, Recall, F1-score, and AP@0.50 than multiple state-of-the-art detection models. YOLO-DGW maintains stable detection performance across diverse geological conditions on three representative railway lines, demonstrating strong cross-region generalization and robust localization of anomalies even under complex overlapping subsurface structures. What are the implications of the main findings? The time-frequency collaborative modeling paradigm offers a scalable and reliable approach for automated detection of subsurface railway defects, reducing reliance on manual interpretation of GPR data and enabling consistent, high accuracy monitoring across large railway networks. The YOLO-DGW architecture provides a transferable framework for interpreting weak and complex subsurface signals, supporting accurate maintenance decision-making and paving the way for intelligent, large-scale railway infrastructure inspection and management. Ground penetrating radar (GPR), as an efficient non-destructive testing technique, plays a crucial role in the structural condition assessment and defect identification of railway ballast. Typical defects such as mud pumping generally exhibit characteristics in B-scan images including weak reflections, blurred boundaries, and irregular structures, which pose significant challenges for stable detection and precise localization using existing methods that rely primarily on spatial feature modeling. Most current deep learning approaches focus on modeling spatial or temporal information, while lacking effective utilization of frequency-domain features, thereby limiting their discriminative capability under complex electromagnetic environments. To address these issues, this paper proposes a single-stage object detection framework, termed YOLO-DGW, based on time-frequency collaborative modeling. Built upon YOLOv8, the proposed method introduces a structure-aware spatial enhancement module to improve the representation of continuous GPR echo structures. Meanwhile, frequency-domain information is incorporated as a modulation prior to guide spatial feature learning, enhancing the model's sensitivity to weak reflections and complex-shaped targets. In addition, A-CIoU loss function is designed to improve localization accuracy and stability for defect regions of varying scales. Experimental results demonstrate that YOLO-DGW achieves an F1-score of 63.06% and an AP@0.50 of 62.07%, representing improvements of approximately 7.41% and 2.8%, respectively, over the strongest baseline method. Compared with several mainstream object detection models, the proposed approach exhibits superior performance in both detection accuracy and cross-region generalization capability. These findings indicate that integrating frequency-domain information into spatial feature learning through a modulation mechanism can effectively enhance the model's ability to discriminate weak-reflection anomalies, providing a novel time-frequency collaborative modeling paradigm for railway GPR defect detection. [ABSTRACT FROM AUTHOR] |
| Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Highlights: What are the main findings? The proposed YOLO-DGW framework, integrating time-frequency collaborative feature modeling, small wavelet-based frequency modulation, and A-CIoU loss, significantly improves detection of weak, irregular mud pumping anomalies in railway GPR B-scan, achieving higher Precision, Recall, F1-score, and AP@0.50 than multiple state-of-the-art detection models. YOLO-DGW maintains stable detection performance across diverse geological conditions on three representative railway lines, demonstrating strong cross-region generalization and robust localization of anomalies even under complex overlapping subsurface structures. What are the implications of the main findings? The time-frequency collaborative modeling paradigm offers a scalable and reliable approach for automated detection of subsurface railway defects, reducing reliance on manual interpretation of GPR data and enabling consistent, high accuracy monitoring across large railway networks. The YOLO-DGW architecture provides a transferable framework for interpreting weak and complex subsurface signals, supporting accurate maintenance decision-making and paving the way for intelligent, large-scale railway infrastructure inspection and management. Ground penetrating radar (GPR), as an efficient non-destructive testing technique, plays a crucial role in the structural condition assessment and defect identification of railway ballast. Typical defects such as mud pumping generally exhibit characteristics in B-scan images including weak reflections, blurred boundaries, and irregular structures, which pose significant challenges for stable detection and precise localization using existing methods that rely primarily on spatial feature modeling. Most current deep learning approaches focus on modeling spatial or temporal information, while lacking effective utilization of frequency-domain features, thereby limiting their discriminative capability under complex electromagnetic environments. To address these issues, this paper proposes a single-stage object detection framework, termed YOLO-DGW, based on time-frequency collaborative modeling. Built upon YOLOv8, the proposed method introduces a structure-aware spatial enhancement module to improve the representation of continuous GPR echo structures. Meanwhile, frequency-domain information is incorporated as a modulation prior to guide spatial feature learning, enhancing the model's sensitivity to weak reflections and complex-shaped targets. In addition, A-CIoU loss function is designed to improve localization accuracy and stability for defect regions of varying scales. Experimental results demonstrate that YOLO-DGW achieves an F1-score of 63.06% and an AP@0.50 of 62.07%, representing improvements of approximately 7.41% and 2.8%, respectively, over the strongest baseline method. Compared with several mainstream object detection models, the proposed approach exhibits superior performance in both detection accuracy and cross-region generalization capability. These findings indicate that integrating frequency-domain information into spatial feature learning through a modulation mechanism can effectively enhance the model's ability to discriminate weak-reflection anomalies, providing a novel time-frequency collaborative modeling paradigm for railway GPR defect detection. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091393 |
