摘要:
地震灾害具有突发性强、灾害环境复杂等特点，地震场景增强现实场景建模对灾害应急救援具有重要意义。现有增强现实场景建模方法在地震灾害场景下存在特征提取不准确，虚实融合建模精度低的问题，难以实现环境复杂的地震灾害现场增强现实场景精准建模。因此，提出结构语义特征约束的地震灾害增强现实场景精准建模方法，首先，剖析地震灾害场景特征，构建地震灾害现场结构语义特征库；其次，提出结构语义约束的地震灾害虚实特征提取，提高虚实图像特征提取精准度；然后，基于提取虚实特征结果进行地震灾害增强现实场景虚实建模；最后，选择受损建筑作为实验案例进行实验分析。实验结果表明，本文基于结构语义约束的地震灾害虚实特征提取方法F1分数达90%，增强现实场景建模结果虚实融合误差指标配准后达到处理前的约20%，证明方法可以有效进行虚实特征的提取以及匹配，实现地震灾害增强现实场景精准建模。
Abstract:
Objectives: Earthquake disaster has become one of the natural disasters that pose a great threat to human society because of its suddenness, destructiveness and wide range of impact. Such disasters are usually accompanied by large-scale building damage, loss of life and property, and complex secondary disasters. Therefore, it puts forward high requirements for the efficiency and accuracy of emergency rescue. In this context, Augmented Reality (AR) technology can provide more intuitive decision-making support for rescue personnel by integrating virtual information into the real scene. Therefore, the efficiency and accuracy of rescue can be significantly improved. However, the existing AR scene modeling technology faces challenges in the special environment of earthquake disaster. The main issues lie in the inaccuracy of feature extraction and insufficient precision of the virtual-real fusion modeling, which makes it difficult to achieve the expected accuracy of AR scene modeling in complex earthquake disaster sites. Methods: An accurate modelling method for earthquake disaster augmented reality scenes with structural semantic feature constraints is proposed. Firstly, the characteristics of earthquake disaster scenes are analyzed, and the semantic feature library of earthquake disaster scene structure is constructed. Secondly, the virtual-real feature extraction of earthquake disasters with structural semantic constraints is proposed to improve the accuracy of virtual-real image feature extraction. Then, based on the results of virtual-real feature extraction, the virtual-real modelling of the earthquake disaster augmented reality scene is carried out. Finally, the damaged building is selected as the experimental case area for experimental analysis. Results: Experimental results indicate that the proposed method achieves an F1-score of 90% for earthquake disaster feature extraction based on structural semantic constraints. Additionally, the virtual-real fusion error in the AR scene modelling results is reduced to approximately 20% of the coarse registration error after alignment. Conclusions: The findings demonstrate that the proposed method effectively extracts and matches virtual-real features, leading to accurate AR scene modelling for earthquake disaster scenarios. At the same time, the proposed method has important application potential for improving the accuracy and reliability of AR applied to disaster emergency response, and can assist onsite rescue personnels to obtain more accurate disaster information.
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