A Laser Plane Attitude Evaluation Method for Rail Profile Measurement Sensors

Wang, Le; Wang, Hao; Han, Qiang; Fang, Yue; Wang, Shengchun; Wang, Ning; Li, Guoqing; Ren, Shuxin

doi:10.3390/s23104586

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…Yet, in industrial manufacturing environments, applying this technique to curved surfaces, such as steel tubes, results in images with uneven brightness, where elevated areas appear brighter (as shown in figure 1). Most research on 3D reconstruction of complex surfaces using multi-line structured light has been conducted under controlled laboratory conditions [23][24][25], where brightness uniformity is typically maintained. In contrast, industrial environments employing high-power lasers struggle with uneven energy distribution along the laser stripes.…”

Section: Introductionmentioning

confidence: 99%

Depth extraction of 3D defects on curved surfaces with multi-line lasers

Jin,

Li,

2024

Meas. Sci. Technol.

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The adoption of computer vision technology has significantly impacted surface defect inspection by providing a non-contact, cost-effective solution that has been widely accepted. Among the various techniques available, three-dimensional (3D) defect inspection using multi-line lasers is notable for its simplicity, high detection speed, and extensive coverage. The accuracy of this method is significantly constrained by the precision of laser stripe extraction. In industrial environments, achieving accurate extraction is hindered by the intricate surface geometries of objects and the challenge of maintaining uniform brightness in multi-line laser stripes. To address these challenges, we propose a novel approach to extract the depth of 3D defects on surfaces using multi-line lasers. Our method combines guided filtering and the Frankle-McCann Retinex algorithms to improve the quality of captured images. We have refined the laser stripe extraction process and proposed an advanced adaptive threshold segmentation technique that utilizes the OTSU method to determine threshold coefficients, followed by secondary segmentation based on a neighborhood search. The extracted laser strips are then processed using the quadratic weighted gray gravity method. Additionally, we proposed an innovative region-growth segmentation method based on neighborhood search that effectively segments individual laser strips. We also design a strategy for determining 3D defect depths in situations where precise camera calibration is challenging. The efficacy of our proposed method was rigorously tested on a hot-rolled seamless steel tube with a diameter of 145 mm. The resulting 3D defect depth exhibited an error of less than 0.5 mm, meeting the stringent standards required for practical applications.

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Section: Introductionmentioning

confidence: 99%

Depth extraction of 3D defects on curved surfaces with multi-line lasers

Jin,

Li,

2024

Meas. Sci. Technol.

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Three-Dimensional Measurement of Full Profile of Steel Rail Cross-Section Based on Line-Structured Light

Zhang

et al. 2023

Electronics

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The wear condition of steel rails directly affects the safety of railway operations. Line-structured-light visual measurement technology is used for online measurement of rail wear due to its ability to achieve high-precision dynamic measurements. However, in dynamic measurements, the random deviation of the measurement plane caused by the vibration of the railcar results in changes in the actual measured rail profile relative to its cross-sectional profile, ultimately leading to measurement deviations. To address these issues, this paper proposes a method for three-dimensional measurement of steel rail cross-sectional profiles based on binocular line-structured light. Firstly, calibrated dual cameras are used to simultaneously capture the profiles of both sides of the steel rail in the same world coordinate system, forming the complete rail profile. Then, considering that the wear at the rail waist is zero in actual operation, the coordinate of the circle center on both sides of the rail waist are connected to form feature vectors. The measured steel rail profile is aligned with the corresponding feature vectors of the standard steel rail model to achieve initial registration; next, the rail profile that has completed the preliminary matching is accurately matched with the target model based on the iterative closest point (ICP) algorithm. Finally, by comparing the projected complete rail profile onto the rail cross-sectional plane with the standard 3D rail model, the amount of wear on the railhead can be obtained. The experimental results indicate that the proposed line-structured-light measurement method for the complete rail profile, when compared to the measurements obtained from the rail wear gauge, exhibits smaller mean absolute deviation (MAD) and root mean square error (RMSE) for both the vertical and lateral dimensions. The MAD values for the vertical and lateral measurements are 0.009 mm and 0.039 mm, respectively, while the RMSE values are 0.011 mm and 0.048 mm. The MAD and RMSE values for the vertical and lateral wear measurements are lower than those obtained using the standard two-dimensional rail profile measurement method. Furthermore, it effectively eliminates the impact of vibrations during the dynamic measurement process, showcasing its practical engineering application value.

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A Laser Plane Attitude Evaluation Method for Rail Profile Measurement Sensors

Cited by 2 publications

References 12 publications

Depth extraction of 3D defects on curved surfaces with multi-line lasers

Depth extraction of 3D defects on curved surfaces with multi-line lasers

Three-Dimensional Measurement of Full Profile of Steel Rail Cross-Section Based on Line-Structured Light

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