用照片和深度数据生成水利设施图谱，成本低且可解释。
A graph generation pipeline for critical infrastructures based on heuristics, images and depth data
- 结合图像与深度信息，用深度学习检测物体并推理关系
- 在两个水利系统上生成的图谱接近真实情况
- 规则可定制，适合高风险基础设施决策
物理关键基础设施（如水电站、能源厂）的虚拟模型用于仿真与数字孪生，以保障服务韧性与连续性。传统方法依赖昂贵的激光扫描点云，需专业技能。本文提出基于摄影测量的原型图生成流程，利用立体相机获取的RGB图像与深度数据，通过深度学习进行目标检测与实例分割，并结合用户定义的启发式规则推断物体间关系。实验在两个液压系统上验证，生成的图谱接近真实结构。该方法虽聚焦液压系统，但可拓展至其他基础设施。用户自定义规则提升了过程透明度，使其适用于高风险决策场景。
原文摘要 · Abstract (English)
Virtual representations of physical critical infrastructures, such as water or energy plants, are used for simulations and digital twins to ensure resilience and continuity of their services. These models usually require 3D point clouds from laser scanners that are expensive to acquire and require specialist knowledge to use. In this article, we present a prototypical graph generation pipeline based on photogrammetry. The pipeline detects relevant objects and predicts their relation using RGB images and depth data generated by a stereo camera. This more cost-effective approach uses deep learning for object detection and instance segmentation of the objects, and employs user-defined heuristics or rules to infer their relations. Results of two hydraulic systems show that this strategy can produce graphs close to the ground truth. While this study focuses on hydraulic systems, the general process can be used to tailor the method to other types of infrastructures and applications. The user-defined rules create transparency qualifying the pipeline to be used in the high stakes decision-making that is required for critical infrastructures.
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