Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching
Bolt connections are widely used as structural connections in civil engineering, mechanical engineering, and bridge construction. However, problems such as loosening, or breakage can occur with bolts after prolonged use. To address the challenges of detecting bolt loosening, this study reviews exist...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10813333/ |
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| author | Peihe Jiang Yuhang Geng Zhongqi Sang Lifeng Lin |
| author_facet | Peihe Jiang Yuhang Geng Zhongqi Sang Lifeng Lin |
| author_sort | Peihe Jiang |
| collection | DOAJ |
| description | Bolt connections are widely used as structural connections in civil engineering, mechanical engineering, and bridge construction. However, problems such as loosening, or breakage can occur with bolts after prolonged use. To address the challenges of detecting bolt loosening, this study reviews existing detection technologies, analyzes their advantages and limitations, and proposes a novel bolt-loosening detection algorithm based on image matching and deep learning. The algorithm comprises the following components: a bolt target detection model based on an improved YOLOv8 algorithm, image correction using perspective transformation, bolt contour detection and image processing, and feature matching to calculate the transformation matrix between images obtained before and after loosening, thereby determining the loosening angle of the bolt. The experiments focused on a rectangular steel plate featuring four M6 standard bolts. The results demonstrate that the bolt target detection model can accurately locate and crop bolt positions and identify loosening angles under various shooting angles, distances, and lighting conditions. At specific shooting angles and appropriate distances, the detection error threshold was less than 2°. Subsequently, experiments conducted in real-world scenarios confirmed the accuracy and feasibility of the proposed algorithm. |
| format | Article |
| id | doaj-art-deafefaf99794d4bb81e5594a6dd1b9c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-deafefaf99794d4bb81e5594a6dd1b9c2025-01-03T00:01:42ZengIEEEIEEE Access2169-35362025-01-01131133114610.1109/ACCESS.2024.352165210813333Bolt Loosening Detection Method Based on Improved YOLOv8 and Image MatchingPeihe Jiang0https://orcid.org/0000-0003-0971-7561Yuhang Geng1https://orcid.org/0009-0007-6142-2329Zhongqi Sang2Lifeng Lin3https://orcid.org/0009-0007-0428-218XSchool of Physics and Electronic Information, Yantai University, Yantai, ChinaSchool of Physics and Electronic Information, Yantai University, Yantai, ChinaSchool of Physics and Electronic Information, Yantai University, Yantai, ChinaSchool of Electronics Engineering, Peking University, Beijing, ChinaBolt connections are widely used as structural connections in civil engineering, mechanical engineering, and bridge construction. However, problems such as loosening, or breakage can occur with bolts after prolonged use. To address the challenges of detecting bolt loosening, this study reviews existing detection technologies, analyzes their advantages and limitations, and proposes a novel bolt-loosening detection algorithm based on image matching and deep learning. The algorithm comprises the following components: a bolt target detection model based on an improved YOLOv8 algorithm, image correction using perspective transformation, bolt contour detection and image processing, and feature matching to calculate the transformation matrix between images obtained before and after loosening, thereby determining the loosening angle of the bolt. The experiments focused on a rectangular steel plate featuring four M6 standard bolts. The results demonstrate that the bolt target detection model can accurately locate and crop bolt positions and identify loosening angles under various shooting angles, distances, and lighting conditions. At specific shooting angles and appropriate distances, the detection error threshold was less than 2°. Subsequently, experiments conducted in real-world scenarios confirmed the accuracy and feasibility of the proposed algorithm.https://ieeexplore.ieee.org/document/10813333/Bolt loosening detectionYOLOv8 algorithmfeature matchingcontour detection |
| spellingShingle | Peihe Jiang Yuhang Geng Zhongqi Sang Lifeng Lin Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching IEEE Access Bolt loosening detection YOLOv8 algorithm feature matching contour detection |
| title | Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching |
| title_full | Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching |
| title_fullStr | Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching |
| title_full_unstemmed | Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching |
| title_short | Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching |
| title_sort | bolt loosening detection method based on improved yolov8 and image matching |
| topic | Bolt loosening detection YOLOv8 algorithm feature matching contour detection |
| url | https://ieeexplore.ieee.org/document/10813333/ |
| work_keys_str_mv | AT peihejiang boltlooseningdetectionmethodbasedonimprovedyolov8andimagematching AT yuhanggeng boltlooseningdetectionmethodbasedonimprovedyolov8andimagematching AT zhongqisang boltlooseningdetectionmethodbasedonimprovedyolov8andimagematching AT lifenglin boltlooseningdetectionmethodbasedonimprovedyolov8andimagematching |