发表机构
Purdue University（普渡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对AI驱动结构缺陷检测的技术门槛问题，推出基于GUI的开源无代码工具YOLOEZ，集成数据标注、训练与推理，其性能优于传统方法，可降低AI在结构健康监测中的应用门槛。
AI 中文摘要
结构健康监测（SHM）在现代工程中至关重要，为基于状态的维护、生命周期评估和预测性决策提供数据。传统上，SHM依赖目视检测来检测裂缝、变形等缺陷；早期计算机视觉（CV）方法，包括阈值分割、边缘检测和手工特征，旨在实现该过程自动化，但对噪声、成像变化和多尺度缺陷高度敏感，限制了其可靠性。近年来机器学习的进展，尤其是卷积神经网络（CNNs）和You Only Look Once（YOLO），提高了缺陷检测准确率并实现了实时分析。然而，由于数据标注、模型训练和部署等技术障碍（通常需要编程专业知识），其在SHM中的应用仍然有限。为解决这一差距，我们推出YOLOEZ——一款基于GUI的开源工具，用于端到端YOLO模型应用。YOLOEZ将数据标注、训练和推理集成到单一界面，无需代码即可实现高性能模型开发，同时支持可复现的工作流。与现有软件和经典图像处理的评估表明，YOLOEZ不仅在大多数检测指标上优于传统方法，还降低了其他现代CV工具存在的采用门槛。通过将准确率与可访问性相结合，YOLOEZ促进了AI驱动监测在预测性维护、数字孪生和智能结构系统中的更广泛应用。
英文摘要
Structural health monitoring (SHM) is essential in modern engineering, providing data for condition-based maintenance, lifecycle assessment, and predictive decision-making. Traditionally, SHM relied on visual inspection to detect defects such as cracks and deformations. Early computer vision (CV) methods, including thresholding, edge detection, and handcrafted features, aimed to automate this process but were highly sensitive to noise, imaging variations, and multiscale defects, limiting their reliability. Recent advances in machine learning, particularly convolutional neural networks (CNNs) and You Only Look Once (YOLO), have improved defect detection accuracy and enabled real-time analysis. However, adoption in SHM remains limited due to technical barriers such as data labeling, model training, and deployment, which typically require programming expertise. To address this gap, we introduce YOLOEZ, an open-source, GUI-based tool for end-to-end YOLO model application. YOLOEZ integrates data labeling, training, and inference into a single interface, enabling high-performance model development without code while supporting reproducible workflows. Evaluation against existing software and classical image processing demonstrates that YOLOEZ not only outperforms traditional methods across most detection metrics, but also lowers adoption barriers present in other modern CV tools. By combining accuracy with accessibility, YOLOEZ facilitates wider use of AI-driven monitoring for predictive maintenance, digital twins, and intelligent structural systems.
Comments11 pages, 7 figures. Accepted to ASME SMASIS 2026 (paper SMASIS2026-190654). Software available at https://github.com/michaelholm6/YOLOEZ
