整合AR和基于深度学习的图像分类,使用CNN进行建筑项目监控
Su-Ling Fan1, Bao-Yu Lin2, Chun-Tin Wu3
1Department of Civil Engineering, and Research Development Center of Construction Law, Tamkang University, No. 151, Ying-Chuan Road, Tamsui, New Taipei County, Taiwan. fansuling@gms.tku.edu.tw.
Scientific reports
|January 21, 2026
概括
手动更新建筑项目的更新是具有挑战性的. 本研究介绍了一种使用增强现实 (AR) 和卷积神经网络 (CNN) 的AI系统,用于准确的实时进度监测,识别施工阶段和材料.
科学领域:
- 建设管理建设管理.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 手动监控建筑项目的进展是劳动密集型,耗时,容易出现不准确.
- 现有的基于人工智能的方法通常依赖于元素计数,无法捕捉到真正的项目状态.
- 有需要的自动化系统,提供精确和实时的建设进展评估.
研究的目的:
- 开发一个自动化系统来监控建筑项目的进展.
- 使用集成技术,准确识别建筑类别和运营阶段.
- 通过超越简单的元素计数来提高进度跟踪的精度.
主要方法:
- 增强现实 (AR) 的整合用于实时数据捕获.
- 应用基于深度学习的图像分类,使用卷积神经网络 (CNN).
- 基于应用材料,系统地确定建筑类别和运行阶段.
主要成果:
- 拟议的系统准确地实时识别建筑类别和运营阶段.
- 通过内部装饰项目的案例研究来证明有效性.
- 通过分析材料和运行状态来实现精确的进度跟踪.
结论:
- 开发的系统显著提高了施工进度监测的准确性.
- 建筑元素和阶段的自动识别可以最大限度地减少人工检查.
- 该方案有效地减少了计划和实际项目进展之间的差异.
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