用仿真热图数据训练AI，自动检测钢板裂缝，减少真实实验依赖。
Autonomous Crack Detection using Deep Learning on Synthetic Thermogram Datasets
- 基于有限元模拟生成合成热图数据，替代真实采集。
- 数据增强提升数据量与多样性，模型在真实数据上验证有效。
- 适合资源受限场景下工业缺陷检测的自动化方案。
在众多科学问题中，需通过大量实验生成数据，且部分任务依赖人工干预。本文聚焦钢板裂缝检测，传统方式依赖人工观察加热后生成的热成像图进行判断。近年来，基于人工智能的方法如卷积神经网络（CNN）被用于替代人工，但这类视觉模型通常需要大量标注数据才能有效，而真实数据的采集过程复杂、耗时耗能，对设备和资源要求高。为此，本文提出一种基于有限元模拟的合成数据生成管道，结合数据增强技术扩充数据规模与多样性。通过微调视觉模型进行推理，并验证该方法在真实实验数据上的泛化能力。结果表明，在特定条件下，该方法可成功实现从仿真到真实场景的迁移，为资源受限环境下的自动化检测提供了可行路径。
原文摘要 · Abstract (English)
In a lot of scientific problems, there is the need to generate data through the running of an extensive number of experiments. Further, some tasks require constant human intervention. We consider the problem of crack detection in steel plates. The way in which this generally happens is through humans looking at an image of the thermogram generated by heating the plate and classifying whether it is cracked or not. There has been a rise in the use of Artificial Intelligence (AI) based methods which try to remove the requirement of a human from this loop by using algorithms such as Convolutional Neural Netowrks (CNN)s as a proxy for the detection process. The issue is that CNNs and other vision models are generally very data-hungry and require huge amounts of data before they can start performing well. This data generation process is not very easy and requires innovation in terms of mechanical and electronic design of the experimental setup. It further requires massive amount of time and energy, which is difficult in resource-constrained scenarios. We try to solve exactly this problem, by creating a synthetic data generation pipeline based on Finite Element Simulations. We employ data augmentation techniques on this data to further increase the volume and diversity of data generated. The working of this concept is shown via performing inference on fine-tuned vision models and we have also validated the results by checking if our approach translates to realistic experimental data. We show the conditions where this translation is successful and how we can go about achieving that.
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