Detecting visual relationships among construction resources plays a pivotal role in understanding complex construction scenes and performing vision-based site monitoring and digitalization. Despite extensive efforts, the propagation effects of different resource-to-resource interactions were overlooked and thus, it is still challenging to precisely detect entangled and intertwined visual relationships from actual construction images. To address the challenge, this study proposes a semantic graph neural network approach that structuralizes construction resources and their entangled interactions in the form of a graph, and simulates the propagation effects using a neural message passing mechanism. The experimental results showed that the proposed approach achieved 77.1% F-score—11.5% higher than the performance of the baseline model. This suggests the positive impacts of the propagation effects and the applicability of the proposed approach. These findings can help understand what are actually happening at construction sites automatically and provide valuable insights for future vision-based monitoring studies.

检测施工资源之间的视觉关系对于理解复杂的施工场景和执行基于视觉的现场监控和数字化具有关键作用。尽管付出了巨大的努力，但忽略了不同资源间交互的传播效果，因此，从实际建筑图像中精确检测纠缠和交织的视觉关系仍然具有挑战性。为了应对这一挑战，本研究提出了一种语义图神经网络方法，将构建资源及其纠缠的交互以图的形式结构化，并使用神经消息传递机制模拟传播效果。实验结果表明，所提出的方法实现了 77.1% 的 F 分数——比基线模型的性能高出 11.5%。这表明传播效应的积极影响和所提出方法的适用性。这些发现可以帮助自动了解建筑工地实际发生的情况，并为未来基于视觉的监测研究提供有价值的见解。