arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
arXiv:2609.13013v1 Announce Type: new Abstract: Flat roofs are among the most influential components of the building envelope, governing both structural performance and thermal efficiency, and thereb...
arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.
arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.
arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.
arXiv:2607. 28695v1 Announce Type: cross Abstract: Here is the plain text version optimized for arXiv's submission form.
The paper introduces YOLOEZ, a no-code, GUI-based tool that streamlines the entire YOLO model workflow—data labeling, training, and inference—for automated structural defect detection. It demonstrates that YOLOEZ outperforms traditional image‑processing methods across most detection metrics while simplifying deployment for users without programming expertise. The tool aims to lower technical barriers in structural health monitoring, enabling broader adoption of AI-driven inspection for predictive maintenance and intelligent structural systems.