用实例分割技术提升复合材料超声检测缺陷识别效率
Effective Defect Detection Using Instance Segmentation for NDI
- 基于Mask-RCNN和YOLO 11的实例分割模型识别超声图像缺陷
- 预处理简化后，检测时间与成本显著降低
- 适合航空航天制造中的自动化无损检测场景
超声检测是航空航天制造中常用的无损检测（NDI）方法。然而，超声扫描数据复杂且规模大，通过人工或机器学习模型识别缺陷极具挑战。本文利用实例分割技术，在代表真实航空航天构件的复合材料面板超声扫描图像中识别缺陷，采用基于Mask-RCNN（Detectron 2）和YOLO 11的两个模型。此外，引入一种简单的统计预处理方法，避免了定制化预处理的繁琐。研究表明，实例分割可显著减少数据预处理时间、检测耗时及整体成本，验证了其在NDI流程中的可行性和有效性。
原文摘要 · Abstract (English)
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.
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