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Effective Defect Detection Using Instance Segmentation for NDI

2025-01-24 · Ashiqur Rahman, Venkata Devesh Reddy Seethi, Austin Yunker, Zachary Kral, Rajkumar Kettimuthu, Hamed Alhoori

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.

📄 PDF Abstract BibTeX arXiv:2501.14149

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Tasks

Defect DetectionInstance Segmentation Semantic Segmentation

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The Second-place Solution for CVPR VISION 23 Challenge Track 1 -- Data Effificient Defect Detection

2023-06-25 · Xian Tao, Zhen Qu, Hengliang Luo, Jianwen Han 외

The Vision Challenge Track 1 for Data-Effificient Defect Detection requires competitors to instance segment 14 industrial inspection datasets in a data-defificient setting. This report introduces the technical details of…

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Semi-supervised Anomaly Detection using AutoEncoders

2020-01-06 · Journal of Computational Vision and Imaging Systems 2020 1 · Manpreet Singh Minhas, John Zelek

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2022-11-03 · Bappaditya Dey, Enrique Dehaerne, Kasem Khalil, Sandip Halder 외

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