标题： 通过 Adapt-WeldNet 和缺陷检测可解释性分析推进海事作业中的焊接缺陷检测 显示英文标题

标题： Advancing Welding Defect Detection in Maritime Operations via Adapt-WeldNet and Defect Detection Interpretability Analysis

摘要： 焊接缺陷检测对于确保石油和天然气行业中管道系统的安全性和可靠性至关重要，尤其是在具有挑战性的海洋和海上环境中。传统的无损检测（NDT）方法往往无法检测到细微或内部缺陷，导致潜在的故障和高昂的停机成本。此外，现有的基于神经网络的缺陷分类方法通常依赖于任意选择的预训练架构，并且缺乏可解释性，这在部署时引发了安全担忧。为了解决这些挑战，本文介绍了“Adapt-WeldNet”，这是一种自适应的焊接缺陷检测框架，系统地评估各种预训练架构、迁移学习策略和自适应优化器，以确定最佳性能的模型和超参数，优化缺陷检测并提供可操作的见解。此外，提出了一种新的缺陷检测可解释性分析（DDIA）框架，以增强系统透明度。DDIA采用可解释的人工智能（XAI）技术，如Grad-CAM和LIME，并结合由认证的ASNT NDE二级专业人员验证的领域特定评估。通过引入人在回路（HITL）方法，并遵循可信人工智能的原则，DDIA确保了缺陷检测系统的可靠性、公平性和问责性，通过专家验证增强了对自动化决策的信心。通过提高性能和可解释性，这项工作增强了焊接缺陷检测系统的信任度、安全性和可靠性，支持海上和海洋环境中的关键操作。 显示英文摘要

摘要： Weld defect detection is crucial for ensuring the safety and reliability of piping systems in the oil and gas industry, especially in challenging marine and offshore environments. Traditional non-destructive testing (NDT) methods often fail to detect subtle or internal defects, leading to potential failures and costly downtime. Furthermore, existing neural network-based approaches for defect classification frequently rely on arbitrarily selected pretrained architectures and lack interpretability, raising safety concerns for deployment. To address these challenges, this paper introduces ``Adapt-WeldNet", an adaptive framework for welding defect detection that systematically evaluates various pre-trained architectures, transfer learning strategies, and adaptive optimizers to identify the best-performing model and hyperparameters, optimizing defect detection and providing actionable insights. Additionally, a novel Defect Detection Interpretability Analysis (DDIA) framework is proposed to enhance system transparency. DDIA employs Explainable AI (XAI) techniques, such as Grad-CAM and LIME, alongside domain-specific evaluations validated by certified ASNT NDE Level II professionals. Incorporating a Human-in-the-Loop (HITL) approach and aligning with the principles of Trustworthy AI, DDIA ensures the reliability, fairness, and accountability of the defect detection system, fostering confidence in automated decisions through expert validation. By improving both performance and interpretability, this work enhances trust, safety, and reliability in welding defect detection systems, supporting critical operations in offshore and marine environments.