Effective Defect Detection Using Instance Segmentation for NDI

TL;DR

This paper demonstrates that instance segmentation models like Mask-RCNN and YOLO can effectively detect defects in ultrasonic scans of aerospace composites, reducing pre-processing and inspection time.

Contribution

It introduces the application of instance segmentation to ultrasonic NDI, showing its feasibility and efficiency in aerospace defect detection.

Findings

Significant reduction in data pre-processing time

Improved defect detection accuracy

Lower inspection costs

Abstract

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.