特征工程与深度学习在自动硬币评级中的比较研究：圣高德斯双鹰金币
Feature Engineering vs. Deep Learning for Automated Coin Grading: A Comparative Study on Saint-Gaudens Double Eagles
AI总结：
本文通过对比特征工程与深度学习方法，发现特征设计在小数据、类别不平衡场景下优于全栈深度学习方案，尤其在硬币评级任务中表现更优。
摘要：
我们挑战了深度学习总是优于传统方法的普遍观念，以自动评级圣高德斯双鹰金币为例。在我们的工作中，我们把基于192个自定义特征的特征驱动人工神经网络（利用Sobel边缘检测和HSV颜色分析提取的特征）与融合EfficientNetV2的混合卷积神经网络，以及一个简单的支持向量机作为对照进行比较。测试1785枚由专家评级的硬币，ANN实现了86%的精确匹配，允许3级容差时达到98%。另一方面，CNN和SVM基本上只是猜测最常见的等级，仅获得31%和30%的精确匹配。尽管CNN在更宽泛的容差指标上表现良好，但这是由于回归中的某些平均技巧，隐藏了它在识别特定等级时完全失败的事实。总的来说，当数据量不足2000例且类别不平衡时，通过特征设计融入真实硬币专家知识优于那些神秘的、全栈深度学习方案。这在其他数据稀少且专业知识更重要的领域同样适用。
英文摘要：
We challenge the common belief that deep learning always trumps older techniques, using the example of grading Saint-Gaudens Double Eagle gold coins automatically. In our work, we put a feature-based Artificial Neural Network built around 192 custom features pulled from Sobel edge detection and HSV color analysis up against a hybrid Convolutional Neural Network that blends in EfficientNetV2, plus a straightforward Support Vector Machine as the control. Testing 1,785 coins graded by experts, the ANN nailed 86% exact matches and hit 98% when allowing a 3-grade leeway. On the flip side, CNN and SVM mostly just guessed the most common grade, scraping by with 31% and 30% exact hits. Sure, the CNN looked good on broader tolerance metrics, but that is because of some averaging trick in regression that hides how it totally flops at picking out specific grades. All told, when you are stuck with under 2,000 examples and lopsided classes, baking in real coin-expert knowledge through feature design beats out those inscrutable, all-in-one deep learning setups. This rings true for other niche quality checks where data's thin and know-how matters more than raw compute.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
