Loosening detection method for hexagonal screws based on error compensation and deep learning
Screws are extensively utilised across various domains. However, the problem of screw loosening not only impacts the stability and safety of engineering structures but also poses the risk of severe accidents and losses. The hollow design of hexagonal screw heads limits the effectiveness of conventio...
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| Published in | Insight (Northampton) Vol. 66; no. 10; pp. 605 - 614 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
The British Institute of Non-Destructive Testing
01.10.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1354-2575 |
| DOI | 10.1784/insi.2024.66.10.605 |
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| Abstract | Screws are extensively utilised across various domains. However, the problem of screw loosening not only impacts the stability and safety of engineering structures but also poses the risk of severe accidents and losses. The hollow design of hexagonal screw heads limits the effectiveness
of conventional methods for detecting screw loosening. Additionally, traditional visual detection methods are influenced by the surrounding environment, affecting detection accuracy. To tackle these challenges, this study introduces a novel approach based on deep learning for detecting loosened
hexagonal screws. This method primarily relies on the you only look once version 8 (YOLOv8) algorithm to accurately detect the coordinates of the four key points of the hexagonal screw. By applying geometric imaging theory, equations are derived for calculating the loosening angle and length
of hexagonal screws. By utilising these equations along with bolt parameters, the degree of loosening is determined. Furthermore, photos captured in low-light environments are enhanced using an improved automatic colour enhancement (ACE) algorithm, which saturates image colours to improve
environmental adaptability. This enhancement facilitates better recognition of hexagonal screws even in dark environments. Four colour enhancement methods are also evaluated based on four criteria. Moreover, by employing a back-propagation (BP) neural network for error compensation, the proposed
method brings predicted values closer to actual values. The experimental results demonstrate an angular identification error of less than 1° and a length identification error of less than 1 mm for loose hexagonal screws. |
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| AbstractList | Screws are extensively utilised across various domains. However, the problem of screw loosening not only impacts the stability and safety of engineering structures but also poses the risk of severe accidents and losses. The hollow design of hexagonal screw heads limits the effectiveness
of conventional methods for detecting screw loosening. Additionally, traditional visual detection methods are influenced by the surrounding environment, affecting detection accuracy. To tackle these challenges, this study introduces a novel approach based on deep learning for detecting loosened
hexagonal screws. This method primarily relies on the you only look once version 8 (YOLOv8) algorithm to accurately detect the coordinates of the four key points of the hexagonal screw. By applying geometric imaging theory, equations are derived for calculating the loosening angle and length
of hexagonal screws. By utilising these equations along with bolt parameters, the degree of loosening is determined. Furthermore, photos captured in low-light environments are enhanced using an improved automatic colour enhancement (ACE) algorithm, which saturates image colours to improve
environmental adaptability. This enhancement facilitates better recognition of hexagonal screws even in dark environments. Four colour enhancement methods are also evaluated based on four criteria. Moreover, by employing a back-propagation (BP) neural network for error compensation, the proposed
method brings predicted values closer to actual values. The experimental results demonstrate an angular identification error of less than 1° and a length identification error of less than 1 mm for loose hexagonal screws. |
| Author | Zhou, Yang Luo, Pengzhan Li, Yang |
| Author_xml | – sequence: 1 givenname: Yang surname: Li fullname: Li, Yang organization: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China – sequence: 2 givenname: Pengzhan surname: Luo fullname: Luo, Pengzhan organization: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China – sequence: 3 givenname: Yang surname: Zhou fullname: Zhou, Yang organization: School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China |
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| Cites_doi | 10.1177/14759217231158540 10.1016/j.ymssp.2019.04.010 10.1177/09544062211039882 10.1109/TIE.2019.2899555 10.1109/JSEN.2023.3271607 10.1016/j.ymssp.2013.05.023 10.1177/1475921719837509 10.1111/mice.13023 |
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| References | (R26_322_311) 2013; 40 (R26_434_644) 2020; 19 (R26_218_644) 2023; 22 (R26_452_460) 2019; 128 (R26_424_207) 2022; 236 (R26_254_518) 2020; 33 (R26_322_426) 2022; 44 (R26_379_610) 2023; 38 (R26_322_127) 2023; 23 (R26_486_495) 2020; 67 (R26_102_483) 2013; 9 |
| References_xml | – volume: 22 start-page: 4264 issn: 14759217 issue: 6 year: 2023 ident: R26_218_644 publication-title: Structural Health Monitoring doi: 10.1177/14759217231158540 – volume: 128 start-page: 588 issn: 08883270 year: 2019 ident: R26_452_460 publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2019.04.010 – volume: 236 start-page: 3277 issn: 09544062 issue: 7 year: 2022 ident: R26_424_207 publication-title: Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science doi: 10.1177/09544062211039882 – volume: 44 start-page: 1303 issn: 03111563 year: 2022 ident: R26_322_426 publication-title: STRUCTURES REPORT- DEPARTMENT OF ARCHITECTURAL SCIENCE UNIVERSITY OF SYDNEY – volume: 9 start-page: 255 issn: 02780895 issue: 12 year: 2013 ident: R26_102_483 publication-title: International Journal of Distributed Sensor Networks – volume: 67 start-page: 1366 issn: 02780046 issue: 2 year: 2020 ident: R26_486_495 publication-title: IEEE Transactions on Industrial Electronics doi: 10.1109/TIE.2019.2899555 – volume: 23 start-page: 13292 issn: 1530437X issue: 12 year: 2023 ident: R26_322_127 publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2023.3271607 – volume: 40 start-page: 589 issn: 08883270 issue: 2 year: 2013 ident: R26_322_311 publication-title: Mechanical Systems and Signal Processing doi: 10.1016/j.ymssp.2013.05.023 – volume: 19 start-page: 105 issn: 14759217 issue: 1 year: 2020 ident: R26_434_644 publication-title: Structural Health Monitoring doi: 10.1177/1475921719837509 – volume: 33 start-page: 40 issn: 05776686 issue: 4 year: 2020 ident: R26_254_518 publication-title: Chinese Journal of Mechanical Engineering – volume: 38 start-page: 2443 issn: 10939687 issue: 17 year: 2023 ident: R26_379_610 publication-title: Computer-Aided Civil and Infrastructure Engineering doi: 10.1111/mice.13023 |
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| Snippet | Screws are extensively utilised across various domains. However, the problem of screw loosening not only impacts the stability and safety of engineering... |
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| SubjectTerms | Ace Algorithm Bp Neural Network Hexagonal Screws Loosening Detection Yolov8 |
| Title | Loosening detection method for hexagonal screws based on error compensation and deep learning |
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