Background: This study presents a real-time mobile system for identifying mixed and
partly covered foreign coins and banknotes using the You Only Look Once version 11
(YOLOv11) deep learning framework. The proposed system addresses practical challenges
faced by travelers and visually impaired individuals when handling multiple currencies.
Methods: The system introduces three novel aspects: (i) simultaneous recognition of
both coins and banknotes from multiple currencies within a single image, even when
items are overlapping or occluded; (ii) a hybrid inference strategy that integrates an
embedded TensorFlow Lite (TFLite) model for on-device detection with an optional server
assisted mode for higher accuracy; and (iii) an integrated currency conversion module
that provides real-time value translation based on current exchange rates. A purpose
build dataset containing 46 denominations classes across four major currencies: US Dollar
(USD), Euro (EUR), Chinese Yuan (CNY), and Korean Won (KRW), was used for training,
including challenging cases of overlap, folding, and partial coverage. Results: Experimental
evaluation demonstrated robust performance under diverse real-world conditions. The
system achieved high detection accuracy and low latency, confirming its suitability for
practical deployment on consumer-grade smartphones. Conclusions: These findings
confirm that the proposed approach achieves an effective balance between portability, robustness, and detection accuracy, making it a viable solution for real-time mixed currency
recognition in everyday scenarios.
Keywords: real-time object identification; YOLOv11; mixed currency recognition; partly
covered objects
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