Augmented reality, deep learning and vision-language query system for construction worker safety - Chen, Haosen; Hou, Lei; Wu, Shaoze; Zhang, Guomin; Zou, Yang; Moon, Sungkon; Bhuiyan, Muhammed (Automation in Construction, 2024)
Low situational awareness contributes to safety incidents in construction. Existing Deep Learning (DL)-based applications lack the capability to provide context-specific and interactive feedback that is essential for workers to fully understand their surrounding environments. This paper proposes the Visual Construction Safety Query (VCSQ) system. The system encompasses real-time Image Captioning (IC), safety-centric Visual Question Answering (VQA), and keyword-based Image-Text Retrieval (ITR), integrated with head-mounted Augmented Reality (AR) devices. System validation includes benchmarks and real-world images. The ITR module posted high recall rates of 0.801 and 0.835 for Recall@5 and @10. The VQA module achieved an 89.7% accuracy rate, and the IC module had a SPICE score of 0.449. Feasibility tests and surveys confirmed the system's practical advantages in different construction scenarios. This study establishes an integration roadmap adaptable to future advancements in interactive DL and immersive AR.
Keywords: Construction safety; Construction workers; Deep learning; Image captioning; Image texts; Language model; Query systems; Question Answering; Text retrieval; Vision-language model; Augmented reality; Construction safety; Deep learning; Vision-language models