@article{bibcite_16259,
  keywords = {Tower crane, Risk perception, YOLO, Knowledge graph, large language model},
  author = {Lingxiao Wang and Jingfeng Yuan and Shu Su and Hongxing Ding and Yu Bai and Miros{\l}aw Skibniewski},
  title = {Fusing enhanced YOLO and knowledge graph-based large language models for automatic risk perception in tower crane operations},
  abstract = {Tower crane operations in construction are inherently hazardous due to complex and dynamic site environments. Enhancing operators{\textquoteright} perceptual and cognitive capabilities is essential for ensuring safety and improving situational awareness. This paper presents an integrated framework that combines an improved YOLOv8 model with a Knowledge Graph (KG)-enhanced large language model to achieve proactive and intelligent safety management. The improved YOLOv8 incorporates attention-based optimization to improve detection accuracy for small targets in tower crane perspectives. A domain-specific safety KG is constructed to represent critical entities, relationships, and operational contexts, and is aligned with a fine-tuned GPT model, enabling semantic reasoning and context-aware hazard interpretation. The integrated system links visual perception with structured knowledge reasoning to provide real-time and interpretable safety feedback. This approach enhances the perception, understanding, and decision-making capabilities of tower crane operators, transforming safety management from reactive monitoring to proactive and intelligent control in complex construction environments.},
  year = {2026},
  journal = {Automation in Construction},
  volume = {183},
  pages = {106823},
  issn = {0926-5805},
  url = {https://www.sciencedirect.com/science/article/pii/S0926580526000646},
  doi = {https://doi.org/10.1016/j.autcon.2026.106823},
}