Abstract Hazards potentially affect the safety of people on construction sites include falls from heights (FFH), trench and scaffold collapse, electric shock and arc flash/arc blast, and failure to use proper personal protective equipment. Such hazards are significant contributors to accidents and fatalities. Computer vision has been used to automatically detect safety hazards to assist with the mitigation of accidents and fatalities. However, as safety regulations are subject to change and become more stringent prevailing computer vision approaches will become obsolete as they are unable to accommodate the adjustments that are made to practice. This paper integrates computer vision algorithms with ontology models to develop a knowledge graph that can automatically and accurately recognise hazards while adhering to safety regulations, even when they are subjected to change. Our developed knowledge graph consists of: (1) an ontological model for hazards: (2) knowledge extraction; and (3) knowledge inference for hazard identification. We focus on the detection of hazards associated with FFH as an example to illustrate our proposed approach. We also demonstrate that our approach can successfully detect FFH hazards in varying contexts from images.

中文翻译：

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识别建筑工地危害的知识图谱：将计算机视觉与本体相结合

摘要 潜在影响施工现场人员安全的危险包括从高处坠落 (FFH)、沟渠和脚手架倒塌、电击和电弧闪光/电弧爆炸以及未能使用适当的个人防护设备。此类危险是事故和死亡的重要原因。计算机视觉已被用于自动检测安全隐患，以帮助减少事故和死亡人数。然而，随着安全法规的变化和变得更加严格，流行的计算机视觉方法将变得过时，因为它们无法适应实践中的调整。本文将计算机视觉算法与本体模型相结合，开发了一个知识图谱，可以在遵守安全法规的同时自动准确识别危险，即使它们会发生变化。我们开发的知识图包括：（1）危害的本体模型：（2）知识提取；(3) 危害识别的知识推理。我们以检测与 FFH 相关的危害为例来说明我们提出的方法。我们还证明了我们的方法可以在不同的图像上下文中成功检测 FFH 危险。