Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces YOLOEZ, an open-source, GUI-based tool that streamlines the application of YOLO models for automated structural defect detection, effectively eliminating the need for programming expertise. By integrating data labeling, model training, and inference into a single interface, YOLOEZ significantly enhances the accessibility of AI-driven inspection workflows. Evaluation results indicate that YOLOEZ outperforms traditional computer vision methods across various detection metrics, thereby facilitating broader adoption in structural health monitoring applications.
YOLOEZ makes high-performance AI-driven defect detection accessible to engineers without programming skills, transforming the landscape of structural health monitoring.
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.