The use of cameras for safety monitoring, progress tracking, and site security has grown significantly on construction and civil infrastructure sites over the past decade. Localization of construction resources is a crucial prerequisite for many applications in automated construction management. However, most existing vision-based methods perform the analysis in the image plane, overlooking the effect of perspective and depth. The manual and labor-intensive process of traditional calibration techniques, as well as the busy and restrictive construction environment, makes this a challenging task. This study proposes a framework for automatic camera calibration with no manual intervention. The framework utilizes convolutional neural networks for geometrical scene analysis and object detection, which are used to estimate the location of horizon line, vertical vanishing point, as well as objects with known height distributions. This enables automatic estimation of camera parameters and retrieval of scale. The proposed framework is evaluated on images from two major construction projects in Melbourne, Australia. Results show that the proposed method achieves a minimum accuracy of 90% in estimating proximity of points on the ground and can facilitate further development of vision-based solutions for safety and productivity analysis.

中文翻译：

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用于施工现场分析的自动远场相机标定

在过去的十年中，用于安全监控、进度跟踪和现场安全的摄像头在建筑和民用基础设施现场的使用显着增加。施工资源的本地化是自动化施工管理中许多应用的关键先决条件。然而，大多数现有的基于视觉的方法在图像平面上进行分析，忽略了透视和深度的影响。传统校准技术的手动和劳动密集型过程，以及繁忙和限制性的施工环境，使这成为一项具有挑战性的任务。本研究提出了一种无需人工干预的自动相机校准框架。该框架利用卷积神经网络进行几何场景分析和目标检测，用于估计地平线的位置，垂直消失点，以及具有已知高度分布的物体。这可以自动估计相机参数和检索比例。拟议的框架是根据澳大利亚墨尔本两个主要建设项目的图像进行评估的。结果表明，所提出的方法在估计地面点的接近度方面的最低准确度为 90%，并且可以促进基于视觉的安全和生产力分析解决方案的进一步开发。