用手工特征比深度学习更准地自动评级古金币。
Feature Engineering vs. Deep Learning for Automated Coin Grading: A Comparative Study on Saint-Gaudens Double Eagles
- 手工设计192个边缘与色彩特征，构建神经网络。
- 准确率达86%，允许3级误差时达98%。
- 小数据、类别不均时专家知识胜过黑箱模型。
我们挑战了深度学习总是优于传统方法的普遍认知，以自动评级圣·戈登斯双鹰金币为例。实验对比了基于192个自定义特征（来自Sobel边缘检测和HSV色彩分析）的人工神经网络（ANN）、融合EfficientNetV2的混合卷积神经网络（CNN）以及简单的支持向量机（SVM）。在1,785枚专家评级硬币上测试，ANN实现86%的精确匹配率，允许3级误差时达98%；而CNN和SVM仅分别达到31%和30%的精确匹配率。尽管CNN在宽松容忍度下表现较好，但这是因回归中的平均化技巧掩盖了其在具体等级判断上的失败。当样本不足2,000且类别分布不均时，通过特征工程融入领域专家知识，优于全端到端的深度学习模型。该结论适用于其他数据稀疏、依赖专业知识的质量评估场景。
原文摘要 · Abstract (English)
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.
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