The real‐time location of construction‐related entities is some of the most useful basic information for automated construction management. However, the implementation of most existing localization methods is limited due to the weak adaptability to construction sites. In this paper, we enhance the monocular vision technique for the localization of construction‐related entities by a sematic and prior knowledge‐based method. A deep learning algorithm is employed to segment the sematic instance in the images, and then the prior knowledge model specifies projection strategies for entities corresponding to various scenarios. Results show that the proposed method achieves satisfying positioning accuracy, is robust in low‐ratio occlusions, and can help facilitate safety early warning, activity recognition, and productivity analysis.

中文翻译：

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与建筑有关的实体的语义和先验辅助单眼定位方法

与建筑相关的实体的实时位置是自动化建筑管理中最有用的一些基本信息。但是，由于对施工现场的适应性较弱，大多数现有的本地化方法的实施受到限制。在本文中，我们通过一种基于知识和先验知识的方法，增强了单目视觉技术对建筑相关实体的定位。采用深度学习算法对图像中的语义实例进行分割，然后先验知识模型为与各种场景相对应的实体指定投影策略。结果表明，该方法达到了满意的定位精度，在低比例遮挡下具有鲁棒性，可以帮助促进安全预警，活动识别和生产率分析。