Application of SIFT operator with binocular vision fusion in Building engineering measurement

With the increasing demand for higher accuracy and reliability in the field of engineering measurement, traditional methods have shown a series of problems when facing complex scenarios and precision measurement tasks. Therefore, a scale invariant feature transformation engineering measurement metho...

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Main Authors: Li Chune, Deng Rui
Format: Article
Language:English
Published: EDP Sciences 2025-01-01
Series:International Journal of Metrology and Quality Engineering
Subjects:
Online Access:https://www.metrology-journal.org/articles/ijmqe/full_html/2025/01/ijmqe240014/ijmqe240014.html
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author Li Chune
Deng Rui
author_facet Li Chune
Deng Rui
author_sort Li Chune
collection DOAJ
description With the increasing demand for higher accuracy and reliability in the field of engineering measurement, traditional methods have shown a series of problems when facing complex scenarios and precision measurement tasks. Therefore, a scale invariant feature transformation engineering measurement method integrating binocular vision is proposed. This study focuses on binocular vision three-dimensional dimension measurement, using two-dimensional chessboard for monocular and binocular calibration to obtain internal and external reference information. At the same time, the scale invariant feature transformation algorithm has been simplified, combined with epipolar geometry to improve matching performance. The experiment showed that the improved scale invariant feature transformation algorithm achieved a matching accuracy of 97%. After fusing binocular vision, the close range matching was improved to 98%, the matching time was reduced to 1.8 seconds, and the number of feature points was reduced to 24. In distance measurement, the minimum error for planar targets was 0.25%, the maximum error for curved targets was 1.08%, and the overall maximum error percentage was 2.24%. The scale invariant feature transformation operator that integrates binocular vision has achieved significant results in engineering measurement, showing higher accuracy and reliability in three-dimensional dimension measurement compared to traditional methods. This innovative method is expected to improve measurement accuracy and reliability, providing a more accurate and feasible solution for three-dimensional dimension measurement in the engineering field.
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institution Kabale University
issn 2107-6847
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spelling doaj-art-c3eab11e7e1c4fdfb6ccd9e20bc806a52025-08-20T03:31:37ZengEDP SciencesInternational Journal of Metrology and Quality Engineering2107-68472025-01-0116510.1051/ijmqe/2025004ijmqe240014Application of SIFT operator with binocular vision fusion in Building engineering measurementLi Chune0Deng Rui1Chongqing Metropolitan College of Science and TechnologyChongqing Metropolitan College of Science and TechnologyWith the increasing demand for higher accuracy and reliability in the field of engineering measurement, traditional methods have shown a series of problems when facing complex scenarios and precision measurement tasks. Therefore, a scale invariant feature transformation engineering measurement method integrating binocular vision is proposed. This study focuses on binocular vision three-dimensional dimension measurement, using two-dimensional chessboard for monocular and binocular calibration to obtain internal and external reference information. At the same time, the scale invariant feature transformation algorithm has been simplified, combined with epipolar geometry to improve matching performance. The experiment showed that the improved scale invariant feature transformation algorithm achieved a matching accuracy of 97%. After fusing binocular vision, the close range matching was improved to 98%, the matching time was reduced to 1.8 seconds, and the number of feature points was reduced to 24. In distance measurement, the minimum error for planar targets was 0.25%, the maximum error for curved targets was 1.08%, and the overall maximum error percentage was 2.24%. The scale invariant feature transformation operator that integrates binocular vision has achieved significant results in engineering measurement, showing higher accuracy and reliability in three-dimensional dimension measurement compared to traditional methods. This innovative method is expected to improve measurement accuracy and reliability, providing a more accurate and feasible solution for three-dimensional dimension measurement in the engineering field.https://www.metrology-journal.org/articles/ijmqe/full_html/2025/01/ijmqe240014/ijmqe240014.htmlbinocular visionscale invariant feature transformationcamera calibrationthree dimensional measurementengineering survey
spellingShingle Li Chune
Deng Rui
Application of SIFT operator with binocular vision fusion in Building engineering measurement
International Journal of Metrology and Quality Engineering
binocular vision
scale invariant feature transformation
camera calibration
three dimensional measurement
engineering survey
title Application of SIFT operator with binocular vision fusion in Building engineering measurement
title_full Application of SIFT operator with binocular vision fusion in Building engineering measurement
title_fullStr Application of SIFT operator with binocular vision fusion in Building engineering measurement
title_full_unstemmed Application of SIFT operator with binocular vision fusion in Building engineering measurement
title_short Application of SIFT operator with binocular vision fusion in Building engineering measurement
title_sort application of sift operator with binocular vision fusion in building engineering measurement
topic binocular vision
scale invariant feature transformation
camera calibration
three dimensional measurement
engineering survey
url https://www.metrology-journal.org/articles/ijmqe/full_html/2025/01/ijmqe240014/ijmqe240014.html
work_keys_str_mv AT lichune applicationofsiftoperatorwithbinocularvisionfusioninbuildingengineeringmeasurement
AT dengrui applicationofsiftoperatorwithbinocularvisionfusioninbuildingengineeringmeasurement