为视障用户设计可离线运行的货币真伪与损毁评估系统
Quantitative Currency Evaluation in Low-Resource Settings through Pattern Analysis to Assist Visually Impaired Users
- 用轻量CNN分类面额，结合损伤指数与模板匹配检测假币
- 8.2万张图像验证，损伤评分能连续反映纸币可用性
- 模型小、速度快，适合手机等低资源设备实时使用
货币识别系统常忽略可用性与真伪评估，尤其在低资源环境中，视障用户和离线验证场景普遍。现有方法多聚焦面额分类，却忽视纸币物理磨损与伪造问题，限制了实际应用。本文提出统一框架，包含三模块：基于轻量CNN的面额分类、通过新型统一货币损伤指数（UCDI）量化损伤、以及基于特征的模板匹配进行假币检测。数据集包含超过82,000张标注图像，涵盖干净、受损及伪造钞票。自研Custom_CNN模型以极少参数实现高分类性能。UCDI指标基于二值掩码损失、色度失真和结构特征损失，提供连续可用性评分。假币检测模块在多种成像条件下均表现可靠。整个框架支持实时、本地化推理，解决受限环境下的部署难题。结果表明，精准、可解释且紧凑的方案可在真实场景中实现包容性货币评估。
原文摘要 · Abstract (English)
Currency recognition systems often overlook usability and authenticity assessment, especially in low-resource environments where visually impaired users and offline validation are common. While existing methods focus on denomination classification, they typically ignore physical degradation and forgery, limiting their applicability in real-world conditions. This paper presents a unified framework for currency evaluation that integrates three modules: denomination classification using lightweight CNN models, damage quantification through a novel Unified Currency Damage Index (UCDI), and counterfeit detection using feature-based template matching. The dataset consists of over 82,000 annotated images spanning clean, damaged, and counterfeit notes. Our Custom_CNN model achieves high classification performance with low parameter count. The UCDI metric provides a continuous usability score based on binary mask loss, chromatic distortion, and structural feature loss. The counterfeit detection module demonstrates reliable identification of forged notes across varied imaging conditions. The framework supports real-time, on-device inference and addresses key deployment challenges in constrained environments. Results show that accurate, interpretable, and compact solutions can support inclusive currency evaluation in practical settings.
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