Effective Defect Detection Using Instance Segmentation for NDI
This addresses the problem of inefficient defect identification in aerospace NDI, but it is incremental as it applies existing instance segmentation models to this domain.
The study tackled defect detection in ultrasonic scans for aerospace manufacturing using instance segmentation, achieving significant reductions in data pre-processing time, inspection time, and costs.
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.