Automated Reed Dent Inspection using Image Processing for Enhanced Quality Assurance in Textile Weaving
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| Format: | Recurso digital |
| Langue: | anglais |
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2025
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| _version_ | 1866901767529168896 |
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| author | J. P. Kharat Atharva Ningnure Ritu Patil Rutuja Patil Sanjana Desai |
| author_facet | J. P. Kharat Atharva Ningnure Ritu Patil Rutuja Patil Sanjana Desai |
| contents | <p><strong>Background:</strong><br>The reed constitutes a critical element in the weaving process, ensuring uniform distribution of warp yarns across the<br>fabric width. Surface defects in reed dents, such as burrs, scratches, and misalignments, can lead to loom stoppages,<br>yarn breakage, and defective fabric structures, thereby adversely affecting productivity. Conventional visual inspection<br>of dents is labour-intensive, highly subjective, and susceptible to oversight of significant defects.<br><strong>Methods:</strong><br>This study presents a computer vision–based automated inspection framework for reed dent quality assessment. Highresolution dent images were processed using edge detection algorithms in conjunction with morphological filtering to<br>localize and characterize surface scratches and imperfections. The developed system subsequently classified the<br>inspected dents into "Accept" and "Reject" categories in accordance with established textile quality standards.<br><strong>Results:</strong><br>Experimental evaluation of the proposed method demonstrated a detection accuracy of 94% for surface scratches. The<br>automated classification process substantially reduced operator dependency, minimized subjective variability, and<br>enhanced inspection throughput compared to traditional manual approaches.<br><strong>Conclusion:</strong><br>The findings confirm that automated inspection leveraging computer vision techniques offers a robust and reliable<br>alternative to conventional practices. Implementation of the proposed system in textile manufacturing environments<br>can facilitate consistent reed dent quality control, improve fabric integrity, reduce loom downtime, and ultimately<br>increase overall production efficiency</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17598606 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Automated Reed Dent Inspection using Image Processing for Enhanced Quality Assurance in Textile Weaving J. P. Kharat Atharva Ningnure Ritu Patil Rutuja Patil Sanjana Desai Computer Vision, Morphological Processing, Reed Dent Inspection, Robotic Automation, Textile Manufacturing <p><strong>Background:</strong><br>The reed constitutes a critical element in the weaving process, ensuring uniform distribution of warp yarns across the<br>fabric width. Surface defects in reed dents, such as burrs, scratches, and misalignments, can lead to loom stoppages,<br>yarn breakage, and defective fabric structures, thereby adversely affecting productivity. Conventional visual inspection<br>of dents is labour-intensive, highly subjective, and susceptible to oversight of significant defects.<br><strong>Methods:</strong><br>This study presents a computer vision–based automated inspection framework for reed dent quality assessment. Highresolution dent images were processed using edge detection algorithms in conjunction with morphological filtering to<br>localize and characterize surface scratches and imperfections. The developed system subsequently classified the<br>inspected dents into "Accept" and "Reject" categories in accordance with established textile quality standards.<br><strong>Results:</strong><br>Experimental evaluation of the proposed method demonstrated a detection accuracy of 94% for surface scratches. The<br>automated classification process substantially reduced operator dependency, minimized subjective variability, and<br>enhanced inspection throughput compared to traditional manual approaches.<br><strong>Conclusion:</strong><br>The findings confirm that automated inspection leveraging computer vision techniques offers a robust and reliable<br>alternative to conventional practices. Implementation of the proposed system in textile manufacturing environments<br>can facilitate consistent reed dent quality control, improve fabric integrity, reduce loom downtime, and ultimately<br>increase overall production efficiency</p> |
| title | Automated Reed Dent Inspection using Image Processing for Enhanced Quality Assurance in Textile Weaving |
| topic | Computer Vision, Morphological Processing, Reed Dent Inspection, Robotic Automation, Textile Manufacturing |
| url | https://doi.org/10.5281/zenodo.17598606 |