Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise-level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. We demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real-world fuel cell catalyst layer samples.

中文翻译：

---

使用空间滤波技术克服噪声背景的燃料电池电极裂纹检测

图像处理是一种功能强大的工具，可以在占用大量变量空间并需要大量数据集的设置中进行快速、自动的数据解析。对难以识别的背景进行特征检测是图像处理的一个子集，有助于从其他主观数据中提取定量指标。裂纹检测和量化是聚合物电解质膜燃料电池质量控制、故障分析和优化的重要能力。这项工作提出了一种执行裂纹检测和量化的技术，克服了常用图像分割技术所面临的挑战。我们演示了如何使用几何过滤噪声级别检测技术来选择二进制阈值，然后从中量化样本的破裂程度。我们使用以编程方式生成的已知裂纹数量的测试图像及其在现实燃料电池催化剂层样品上的性能来证明我们技术的准确性。