Real-Time Identification of Mixed and Partly Covered Foreign Currency Using YOLOv11 Object Detection

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.