Monocular-Vision-Based Method for Locating the Center of Anchor Holes on Steel Belts in Coal Mine Roadways

The precise positioning of anchoring-hole centers on the steel belts used for anchor support in coal mines is essential for improving the automation and efficiency of roadway support. To address the issues of poor positioning accuracy and the low support efficiency caused by the manual determination of anchoring-hole-center positions, this paper proposes a monocular-vision-based method for locating anchoring-hole centers. Firstly, a laser pointer and an industrial camera are used to build an anchoring-hole positioning device, and its visual positioning model is constructed to achieve the automatic and precise localization of the anchoring-hole center. Secondly, to overcome the difficulty of obtaining high-precision spot centers using edge and grayscale information-based spot extraction methods, a spot center extraction method based on two-dimensional arctangent function fitting is proposed, achieving high precision and the stable acquisition of spot pixel coordinates. The experimental results show that the average measurement errors of the anchoring-hole centers in the camera’s coordinate system along the X-axis, Y-axis, and Z-axis are 3.36 mm, 3.30 mm, and 5.75 mm, respectively, with maximum errors of 4.23 mm, 4.39 mm, and 6.63 mm. The average measurement errors of the steel belt’s pitch, yaw, and roll angles in the camera’s coordinate system are 0.16°, 0.16°, and 0.08°, respectively, with maximum errors of 0.21°, 0.27°, and 0.13°. The proposed method can achieve the precise localization of anchoring holes, improve the efficiency of roadway support, and provide new insights for the automation and intelligentization of roadway anchor support.

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Publication Details

Journal
Applied Sciences
Published
2024-08-12
DOI
https://doi.org/10.3390/app14167080
Primary Topic
Advanced Measurement and Detection Methods
Type
article
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article

Monocular-Vision-Based Method for Locating the Center of Anchor Holes on Steel Belts in Coal Mine Roadways

Mengyu Lei, Xuhui Zhang, Xin Chen
Applied Sciences
Advanced Measurement and Detection Methods
article

Monocular-Vision-Based Method for Locating the Center of Anchor Holes on Steel Belts in Coal Mine Roadways

Mengyu Lei, Xuhui Zhang, Xin Chen
article en

Abstract

The precise positioning of anchoring-hole centers on the steel belts used for anchor support in coal mines is essential for improving the automation and efficiency of roadway support. To address the issues of poor positioning accuracy and the low support efficiency caused by the manual determination of anchoring-hole-center positions, this paper proposes a monocular-vision-based method for locating anchoring-hole centers. Firstly, a laser pointer and an industrial camera are used to build an anchoring-hole positioning device, and its visual positioning model is constructed to achieve the automatic and precise localization of the anchoring-hole center. Secondly, to overcome the difficulty of obtaining high-precision spot centers using edge and grayscale information-based spot extraction methods, a spot center extraction method based on two-dimensional arctangent function fitting is proposed, achieving high precision and the stable acquisition of spot pixel coordinates. The experimental results show that the average measurement errors of the anchoring-hole centers in the camera’s coordinate system along the X-axis, Y-axis, and Z-axis are 3.36 mm, 3.30 mm, and 5.75 mm, respectively, with maximum errors of 4.23 mm, 4.39 mm, and 6.63 mm. The average measurement errors of the steel belt’s pitch, yaw, and roll angles in the camera’s coordinate system are 0.16°, 0.16°, and 0.08°, respectively, with maximum errors of 0.21°, 0.27°, and 0.13°. The proposed method can achieve the precise localization of anchoring holes, improve the efficiency of roadway support, and provide new insights for the automation and intelligentization of roadway anchor support.

Applied SciencesVol. 14(16)
Xi'an University of Science and Technology (CN)
Openalex Percentile: Top 90%
Advanced Measurement and Detection Methods
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