用深度学习识别尼日利亚奈拉纸币面额，移动端模型效果最佳。
Performance Comparison of Deep Learning Techniques in Naira Classification
- 基于不同架构训练模型，以MobileNetV2表现最优。
- 训练准确率达90.75%，验证准确率达87.04%。
- 适合用于自动点钞、助盲设备等实际场景。
奈拉是尼日利亚日常交易的官方货币。本研究部署并评估了深度学习（DL）模型对奈拉纸币面额的分类性能。基于1,808张在不同条件下拍摄的奈拉纸币图像构建多样化数据集，采用多种网络架构训练模型，其中MobileNetV2表现最佳，在分类任务中达到90.75%的训练准确率和87.04%的验证准确率，且在各种场景下均表现出色。该模型在自动现金处理系统、分拣系统及视障人士辅助技术等领域具有重要应用潜力，有助于提升尼日利亚金融交易的安全性与效率。
原文摘要 · Abstract (English)
The Naira is Nigeria's official currency in daily transactions. This study presents the deployment and evaluation of Deep Learning (DL) models to classify Currency Notes (Naira) by denomination. Using a diverse dataset of 1,808 images of Naira notes captured under different conditions, trained the models employing different architectures and got the highest accuracy with MobileNetV2, the model achieved a high accuracy rate of in training of 90.75% and validation accuracy of 87.04% in classification tasks and demonstrated substantial performance across various scenarios. This model holds significant potential for practical applications, including automated cash handling systems, sorting systems, and assistive technology for the visually impaired. The results demonstrate how the model could boost the Nigerian economy's security and efficiency of financial transactions.
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