The Path to NDE 4.0: A Review of Algorithms, Techniques, and Strategies for Autonomous Inspection

Computer Science > Machine Learning arXiv:2610.31704 (cs) [Submitted on 21 Oct 2026] Title: The Path to NDE 4.0: A Review of Algorithms, Techniques, and Strategies for Autonomous Inspection. Authors: Ashiqur Rahmana, Venkata Devesh Reddy Seethi, Zachary Kral, Austin Yunker, Rajkumar Kettimuthu, Hamed Alhoori.

计算机科学 > 机器学习 arXiv:2610.31704 (cs) [提交于 2026 年 10 月 21 日] 标题：《通往 NDE 4.0 之路：自主检测算法、技术与策略综述》。作者：Ashiqur Rahmana, Venkata Devesh Reddy Seethi, Zachary Kral, Austin Yunker, Rajkumar Kettimuthu, Hamed Alhoori。

Abstract: Non-Destructive Inspection (NDI) is a cornerstone of modern quality assurance, critical for ensuring the safety and reliability of infrastructure and manufactured components without causing damage. This paper presents a comprehensive review of the current NDI landscape, analyzing the field through four distinct lenses, namely, computational algorithms, physical techniques, industrial applications, and research objectives.

摘要：无损检测（NDI）是现代质量保证的基石，对于在不造成损坏的情况下确保基础设施和制造组件的安全性和可靠性至关重要。本文对当前的 NDI 格局进行了全面综述，通过计算算法、物理技术、工业应用和研究目标这四个不同的视角对该领域进行了分析。

We synthesize a broad range of academic literature to illustrate the rapid evolution from manual, analog interpretation to a digitized, data-centric paradigm known as NDE 4.0. Our analysis highlights the dominance of ultrasound and visual inspection as primary physical techniques, while revealing a significant shift toward deep learning and generative models for automated defect recognition.

我们综合了广泛的学术文献，阐述了从人工模拟判读向被称为 NDE 4.0 的数字化、以数据为中心的范式的快速演变。我们的分析强调了超声和视觉检测作为主要物理技术的主导地位，同时揭示了向用于自动缺陷识别的深度学习和生成模型的重要转变。

Despite these technological strides, the industry faces critical bottlenecks, notably the scarcity of standardized open-access datasets and the “black box” nature of AI models, which hinders widespread adoption. By charting these developments and identifying key research gaps, such as the need for explainable AI (XAI) and standardized digital twin protocols, this review offers a strategic roadmap for future research.

尽管取得了这些技术进步，该行业仍面临关键瓶颈，特别是标准化开放获取数据集的匮乏以及人工智能模型的“黑箱”性质，这阻碍了其广泛应用。通过梳理这些发展并确定关键的研究空白（例如对可解释人工智能 (XAI) 和标准化数字孪生协议的需求），本综述为未来的研究提供了战略路线图。

It concludes that the future of NDI research should focus on the successful integration of intelligent, autonomous fusion of inspection data with advanced sensor technologies into asset lifecycle management.

结论指出，NDI 研究的未来应聚焦于将检测数据的智能自主融合与先进传感器技术成功集成到资产全生命周期管理中。