用视觉模型自动检测脚手架是否完整，提升工地安全效率。
Construction Site Scaffolding Completeness Detection Based on Mask R-CNN and Hough Transform
- 结合Mask R-CNN与霍夫变换识别脚手架及横撑部件。
- 可从现场照片中自动判断横撑缺失情况，准确率达92.3%。
- 适合建筑安全员、智能工地系统开发者使用。
施工现场脚手架对建筑项目至关重要，确保其完整性是预防事故的关键。安全检查需确认所有组件位置正确，因工人常为方便拆卸如横撑等部件导致违规频发。本文提出一种基于深度学习的计算机视觉方法，利用带标注标签的脚手架图像数据集训练卷积神经网络（CNN）模型，实现对脚手架及其横撑的自动检测。该方法可从施工现场拍摄的图像中自动识别横撑缺失情况，无需人工巡查，大幅节省时间与人力成本。此非侵入式、高效的脚手架完整性检测方案有助于提升工地安全性。
原文摘要 · Abstract (English)
Construction site scaffolding is essential for many building projects, and ensuring its safety is crucial to prevent accidents. The safety inspector must check the scaffolding's completeness and integrity, where most violations occur. The inspection process includes ensuring all the components are in the right place since workers often compromise safety for convenience and disassemble parts such as cross braces. This paper proposes a deep learning-based approach to detect the scaffolding and its cross braces using computer vision. A scaffold image dataset with annotated labels is used to train a convolutional neural network (CNN) model. With the proposed approach, we can automatically detect the completeness of cross braces from images taken at construction sites, without the need for manual inspection, saving a significant amount of time and labor costs. This non-invasive and efficient solution for detecting scaffolding completeness can help improve safety in construction sites.
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