用半合成裂缝图像提升地震后裂纹检测精度
Improving Post-Earthquake Crack Detection using Semi-Synthetic Generated Images
- 通过参数化元标注在真实建筑3D模型上生成裂缝图像
- 混合真实与半合成图像训练使检测准确率显著提升
- 适合需要大量标注数据的灾害评估研究者使用
地震后快速评估受灾区域安全至关重要。基于计算机视觉和深度学习的损毁检测系统可辅助专家完成任务，但缺乏大规模标注数据制约了其发展。本文提出一种生成半合成图像的方法，用于损毁检测系统的数据增强。重点针对常见且具指示性的裂纹，利用参数化元标注指导在真实建筑3D模型上生成裂纹。通过迭代调整元标注的控制参数，生成最优适配检测器性能的图像。对比实验表明，使用真实与半合成图像联合训练的检测系统，优于仅用真实图像训练的系统。
原文摘要 · Abstract (English)
Following an earthquake, it is vital to quickly evaluate the safety of the impacted areas. Damage detection systems, powered by computer vision and deep learning, can assist experts in this endeavor. However, the lack of extensive, labeled datasets poses a challenge to the development of these systems. In this study, we introduce a technique for generating semi-synthetic images to be used as data augmentation during the training of a damage detection system. We specifically aim to generate images of cracks, which are a prevalent and indicative form of damage. The central concept is to employ parametric meta-annotations to guide the process of generating cracks on 3D models of real-word structures. The governing parameters of these meta-annotations can be adjusted iteratively to yield images that are optimally suited for improving detectors' performance. Comparative evaluations demonstrated that a crack detection system trained with a combination of real and semi-synthetic images outperforms a system trained on real images alone.
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