A mine image stitching method based on improved best seam-line

ZHANG Xuhui; WANG Yue; YANG Wenjuan; CHEN Xin; ZHANG Chao; HUANG Mengyao; LIU Yanhui; YANG Junhao

doi:10.13272/j.issn.1671-251x.2023120003

Volume 50 Issue 4

Mar. 2024

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Journal of Mine Automation > 2024 > 50(4): 9-17. > DOI: 10.13272/j.issn.1671-251x.2023120003

ZHANG Xuhui, WANG Yue, YANG Wenjuan, et al. A mine image stitching method based on improved best seam-line[J]. Journal of Mine Automation，2024，50（4）：9-17. DOI: 10.13272/j.issn.1671-251x.2023120003

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A mine image stitching method based on improved best seam-line

1.
College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China
2.
Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control, Xi'an University of Science and Technology, Xi'an 710054, China

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Received Date: December 01, 2023
Revised Date: April 14, 2024
Available Online: May 09, 2024

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Abstract

The harsh environment of high dust and low lighting in the coal mine underground excavation working face results in low signal-to-noise ratio of the image, and a serious reduction in the number of effective feature points. The processed image has significant color difference and noise. When using the best seam-line algorithm for image stitching, there are problems such as fine section misalignment, unnatural transitions at the seam line, or obvious stitching traces. In order to solve the above problems, a mine image stitching method based on improved best seam-line is proposed. Firstly, the original image is subjected to HSV spatial transformation, and an improved Retinex algorithm is used for enhancement on the luminance component. Bilateral filtering is used instead of the center surround function to solve the halo problem caused by large brightness differences. The number of feature points extracted is effectively increased through the enhancement algorithm. Secondly, the SIFT algorithm is used to extract feature points, and cosine distance is used as the matching degree indicator. The method introduces pixel cosine similarity as a constraint, and uses morphological operations to improve color difference intensity, uses dynamic programming to search for the best seam-line to avoid misalignment at image stitching. Finally, combined with the gradual in and out algorithm, the image transition is smooth to achieve image fusion of the underground excavation working face. Experimental verification is conducted by simulating the actual working environment underground. The results show that the mine image stitching method based on the improved best seam-line avoids the phenomenon of misalignment stitching caused by color differences and noise compared to the traditional best seam-line algorithm. The image transition at the stitching seam is more natural, avoiding the generation of 'ghosts' and obvious stitching seams. The average gradient of the image is increased by about 2.38%, and the stitching time is increased by about 32.5%, making the fusion area smoother and more natural, improving the stitching quality.
- excavation working face,
- image stitching,
- image enhancement,
- best seam-line,
- Retinex algorithm,
- improved energy function,
- pixel cosine similarity

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A mine image stitching method based on improved best seam-line