Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection
The automated detection of yarn margins is crucial for ensuring the continuity and quality of production in textile workshops. Traditional methods rely on workers visually inspecting the yarn margin to determine the timing of replacement; these methods fail to provide real-time data and cannot meet...
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MDPI AG
2025-01-01
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| author | Ke Le Yanhong Yuan |
| author_facet | Ke Le Yanhong Yuan |
| author_sort | Ke Le |
| collection | DOAJ |
| description | The automated detection of yarn margins is crucial for ensuring the continuity and quality of production in textile workshops. Traditional methods rely on workers visually inspecting the yarn margin to determine the timing of replacement; these methods fail to provide real-time data and cannot meet the precise scheduling requirements of modern production. The complex environmental conditions in textile workshops, combined with the cylindrical shape and repetitive textural features of yarn bobbins, limit the application of traditional visual solutions. Therefore, we propose a visual measurement method based on the geometric characteristics of binocular imaging: First, all contours in the image are extracted, and the distance sequence between the contours and the centroid is extracted. This sequence is then matched with a predefined template to identify the contour information of the yarn bobbin. Additionally, four equations for the tangent line from the camera optical center to the edge points of the yarn bobbin contour are established, and the angle bisectors of each pair of tangents are found. By solving the system of equations for these two angle bisectors, their intersection point is determined, giving the radius of the yarn bobbin. This method overcomes the limitations of monocular vision systems, which lack depth information and suffer from size measurement errors due to the insufficient repeat positioning accuracy when patrolling back and forth. Next, to address the self-occlusion issues and matching difficulties during binocular system measurements caused by the yarn bobbin surface’s repetitive texture, an imaging model is established based on the yarn bobbin’s cylindrical characteristics. This avoids pixel-by-pixel matching in binocular vision and enables the accurate measurement of the remaining yarn margin. The experimental data show that the measurement method exhibits high precision within the recommended working distance range, with an average error of only 0.68 mm. |
| format | Article |
| id | doaj-art-4406265bfe6c45a8a5248bb3178ce689 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-4406265bfe6c45a8a5248bb3178ce6892025-01-24T13:48:34ZengMDPI AGSensors1424-82202025-01-0125233910.3390/s25020339Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining DetectionKe Le0Yanhong Yuan1School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, ChinaThe automated detection of yarn margins is crucial for ensuring the continuity and quality of production in textile workshops. Traditional methods rely on workers visually inspecting the yarn margin to determine the timing of replacement; these methods fail to provide real-time data and cannot meet the precise scheduling requirements of modern production. The complex environmental conditions in textile workshops, combined with the cylindrical shape and repetitive textural features of yarn bobbins, limit the application of traditional visual solutions. Therefore, we propose a visual measurement method based on the geometric characteristics of binocular imaging: First, all contours in the image are extracted, and the distance sequence between the contours and the centroid is extracted. This sequence is then matched with a predefined template to identify the contour information of the yarn bobbin. Additionally, four equations for the tangent line from the camera optical center to the edge points of the yarn bobbin contour are established, and the angle bisectors of each pair of tangents are found. By solving the system of equations for these two angle bisectors, their intersection point is determined, giving the radius of the yarn bobbin. This method overcomes the limitations of monocular vision systems, which lack depth information and suffer from size measurement errors due to the insufficient repeat positioning accuracy when patrolling back and forth. Next, to address the self-occlusion issues and matching difficulties during binocular system measurements caused by the yarn bobbin surface’s repetitive texture, an imaging model is established based on the yarn bobbin’s cylindrical characteristics. This avoids pixel-by-pixel matching in binocular vision and enables the accurate measurement of the remaining yarn margin. The experimental data show that the measurement method exhibits high precision within the recommended working distance range, with an average error of only 0.68 mm.https://www.mdpi.com/1424-8220/25/2/339yarn margin detectionbinocular visionrotary bodyimaging model |
| spellingShingle | Ke Le Yanhong Yuan Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection Sensors yarn margin detection binocular vision rotary body imaging model |
| title | Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection |
| title_full | Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection |
| title_fullStr | Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection |
| title_full_unstemmed | Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection |
| title_short | Based on the Geometric Characteristics of Binocular Imaging for Yarn Remaining Detection |
| title_sort | based on the geometric characteristics of binocular imaging for yarn remaining detection |
| topic | yarn margin detection binocular vision rotary body imaging model |
| url | https://www.mdpi.com/1424-8220/25/2/339 |
| work_keys_str_mv | AT kele basedonthegeometriccharacteristicsofbinocularimagingforyarnremainingdetection AT yanhongyuan basedonthegeometriccharacteristicsofbinocularimagingforyarnremainingdetection |