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Counterfeit currency creates a significant financial and security threat, often mimicking genuine notes so precisely that the human eye struggles to discern the differences. This issue becomes even mo...re severe for the visually impaired, who struggle to distinguish between authentic and counterfeit banknotes. To overcome this problem, a new two-phase approach is proposed that uses the You Only Look Once-Neural Architecture System (YOLO-NAS) to detect and verify Indian rupee notes under ultraviolet (UV) light. This model comprises two phases: In the first phase, the observable and invisible characteristics of a currency note are identified. In contrast the second phase authenticates it based on advanced security features that are exclusively detectable under ultraviolet (UV) light. The model's performance is evaluated on two distinct datasets: the Indian and Thai banknotes dataset and the self-designed dataset. The first experiment was conducted on the Indian and Thai banknote datasets, achieving an accuracy of 91.02%. Then, another experiment was performed on a self-created dataset, yielding an accuracy of 93.99%. Furthermore, an audio-based output system is integrated to assist visually impaired individuals in identifying and verifying banknotes. Experimental results indicate that the proposed method enhances counterfeit detection, making it suitable for practical use.続きを見る
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Mendeley出力
p1633-1653