Effective Defect Detection Using Instance Segmentation for NDI
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917901486784512 |
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| author | Rahman, Ashiqur Seethi, Venkata Devesh Reddy Yunker, Austin Kral, Zachary Kettimuthu, Rajkumar Alhoori, Hamed |
| author_facet | Rahman, Ashiqur Seethi, Venkata Devesh Reddy Yunker, Austin Kral, Zachary Kettimuthu, Rajkumar Alhoori, Hamed |
| contents | Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace. We used two models based on Mask-RCNN (Detectron 2) and YOLO 11 respectively. Additionally, we implemented a simple statistical pre-processing technique that reduces the burden of requiring custom-tailored pre-processing techniques. Our study demonstrates the feasibility and effectiveness of using instance segmentation in the NDI pipeline by significantly reducing data pre-processing time, inspection time, and overall costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14149 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Effective Defect Detection Using Instance Segmentation for NDI Rahman, Ashiqur Seethi, Venkata Devesh Reddy Yunker, Austin Kral, Zachary Kettimuthu, Rajkumar Alhoori, Hamed Computer Vision and Pattern Recognition Machine Learning Ultrasonic testing is a common Non-Destructive Inspection (NDI) method used in aerospace manufacturing. However, the complexity and size of the ultrasonic scans make it challenging to identify defects through visual inspection or machine learning models. Using computer vision techniques to identify defects from ultrasonic scans is an evolving research area. In this study, we used instance segmentation to identify the presence of defects in the ultrasonic scan images of composite panels that are representative of real components manufactured in aerospace. We used two models based on Mask-RCNN (Detectron 2) and YOLO 11 respectively. Additionally, we implemented a simple statistical pre-processing technique that reduces the burden of requiring custom-tailored pre-processing techniques. Our study demonstrates the feasibility and effectiveness of using instance segmentation in the NDI pipeline by significantly reducing data pre-processing time, inspection time, and overall costs. |
| title | Effective Defect Detection Using Instance Segmentation for NDI |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.14149 |