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.

Have you ever tried training an object detection model using a custom dataset of your own choice from scratch? If yes, you'd know how tedious the process would be. We need to start with building a model using a Feature Pyramid Network combined with a Region Proposal Network if we opt for region proposal based methods such as Faster R-CNN or we can also use one-shot detector algorithms like SSD and YOLO. Either of them is a bit complicated to work with if we want to implement it from scratch. We need a framework where we can use state-of-the-art models such as Fast, Faster, and Mask R-CNNs with ease.