Moroccan Coin Detection and Classification Using YOLOv8: A Deep Learning Approach

The manual counting of bulk coinage is tedious and prone to error; hence, an automated solution is highly beneficial for numerous applications, including but not limited to gaming machines, vending machines, and cash-intensive enterprises. With the advancement of machine learning techniques, automatic detection and valuation of coins have become increasingly viable. In this study, we present the development of a deep learning system for the detection and classification of Moroccan coins based on the YOLOv8 object detection model. A custom dataset was manually created by gathering images of Moroccan coins from the web and capturing them using smartphone cameras. The experimental results show that our model achieves high accuracy in detecting and discriminating between various denominations of Moroccan coins. For real-world applicability, we further developed the model, making it lightweight, converting it to TensorFlow Lite format, and deploying it on Android smartphones. This allows an efficient and responsive solution by enabling fast inference directly on the device without any cloud resources. The system thus represents a practical end-to-end framework for automatic coin detection and valuation.