A method for in-field railhead crack detection using digital image correlation

Meyer, Knut Andreas; Gren, Daniel; Ahlström, Johan; Ekberg, Anders

doi:10.1080/23248378.2021.2021455

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…However, these two methods have high requirements for the concrete surface, requiring a relatively flat surface and specialized sensors to receive the reflected signals. In recent years, automated crack detection systems based on digital images have gradually been applied to the maintenance of railroad ties [3] . The method is mainly for threshold segmentation [4] , feature (b) (a) extraction [5] , and other operations on the image to realize the recognition, segmentation, and detection of cracks.…”

Section: Introductionmentioning

confidence: 99%

High-speed railway concrete slab crack detection based on image semantic segmentation method

Man,

Mao,

Lai

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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Rapid and accurate detection of surface cracks on high-speed railway concrete slabs is of great significance for maintaining the safety and reliability of railway structures. However, existing crack detection methods suffer from segmentation insufficiency and weak anti-interference capabilities. In this paper, a novel automated detection method based on image semantic segmentation is proposed to achieve segmentation detection and quantitative analysis of surface cracks on high-speed railway concrete track slabs. First, the crack images are preprocessed to enhance the crack edge features through white balance and filtering. Then, an image-driven semantic segmentation network CBAM-U-Net is utilized for pixel-level crack detection on the sampled crack images. The network reduces the computational effort and improves the accuracy mainly by improving the 'skip connection' information interaction mechanism of the U-Net network and introducing CBAM blocks. Finally, by counting the pixel number of the segmentation area, the crack can be quantitatively evaluated. The experimental result demonstrates the effectiveness of the proposed method, with an average Dice coefficient of 73.35%. This method provides a new strategy for automated crack detection on high-speed railway concrete track slabs.

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Section: Introductionmentioning

confidence: 99%

High-speed railway concrete slab crack detection based on image semantic segmentation method

Man,

Mao,

Lai

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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“…DIC may be used to confirm the cracking point determined by the AE testing [ 34 , 35 ], as well as identification of different damage mechanisms (plastic deformation, crack initiation, and propagation) based on comparison AE and DIC data [ 36 , 37 , 38 , 39 ]. DIC is also suitable for full-scale testing, including railway equipment [ 40 ], but this application is still limited due to limited field of view, specific calibration, etc.…”

Section: Introductionmentioning

confidence: 99%

Combined Acoustic Emission and Digital Image Correlation for Early Detection and Measurement of Fatigue Cracks in Rails and Train Parts under Dynamic Loading

Мачихин

Poroykov

Bardakov

et al. 2022

Sensors

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Fatigue crack in rails and cyclic-loaded train parts is a contributory factor in multiple railroad accidents. We address the problem of crack detection and measurement at early stages, when total failure has not yet occurred. We propose to combine acoustic emission (AE) testing for prediction of crack growth with digital image correlation (DIC) for its accurate quantitative characterization. In this study, we imitated fatigue crack appearance and growth in samples of railway rail and two train parts by cyclic loading, and applied these two techniques for inspection. Experimental results clearly indicate the efficiency of AE in the early detection of fatigue cracks, and excellent DIC capabilities in terms of geometrical measurements. Combination of these techniques reveals a promising basis for real-time and non-destructive monitoring of rails and train parts.

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