RCNNS DRIVEN IMAGE PROCESSING FOR AUTOMATED COIN IDENTIFICATION | IJORET – Volume 11- Issue 2 | IJORETV11I2P5

This project presents a deep learning-based system for automatic coin identification using Region-based Convolutional Neural Networks (RCNNs). The goal of the system is to accurately detect and classify coins from images in real-world conditions. Coin recognition is widely used in applications such as vending machines, banking systems, and automated financial services, where speed and accuracy are essential. However, identifying coins is difficult due to similarities in appearance, variations in design, and environmental factors like lighting, noise, and coin wear. To overcome these challenges, the proposed system uses an RCNN model that focuses on identifying specific regions in an image where coins are present. Unlike traditional methods that depend on manually selected features, this approach automatically learns important visual characteristics such as patterns, edges, and symbols directly from the data. The system also includes preprocessing steps like image resizing, noise reduction, and normalization to improve input quality and enhance detection performance. A dataset containing images of different coin denominations is used to train and evaluate the model. The trained system is capable of detecting multiple coins in a single image and classifying them accurately, even under varying conditions such as rotation, background complexity, and partial occlusion. The results show that the proposed approach provides better accuracy and reliability compared to conventional techniques. Overall, this project demonstrates how advanced deep learning methods can be effectively applied to image processing tasks. The developed system offers a practical and scalable solution for automated coin recognition and can be further extended for real-time applications in financial automation and smart systems.

In this project, an automated coin identification system has been developed by integrating image processing techniques with deep learning algorithms. The system is designed to detect and classify coins from input images with high accuracy. The methodology involved several stages, including image acquisition, preprocessing, feature extraction, region proposal generation, classification, and output visualization. The use of RCNN enabled the system to perform both detection and classification tasks effectively. Unlike traditional methods, which rely on manual feature extraction, the proposed system automatically learns features from the data, making it more robust and adaptable to different conditions. The implementation of the system in MATLAB provided a flexible and efficient environment for developing and testing the model. The objectives defined at the beginning of the project have been successfully achieved. The system is capable of accurately detecting coins in input images and classifying them based on their denominations. It also demonstrates the ability to handle multiple coins in a single image, even when they are placed in complex backgrounds. The preprocessing techniques applied to the images improved the quality of the input data, which contributed to better model performance. The evaluation of the system using performance metrics such as accuracy, precision, and recall confirms its effectiveness and reliability. The results of the project indicate that deep learning techniques, particularly RCNN, provide a powerful solution for coin identification problems. The system achieves high accuracy and performs well under various conditions, including different lighting environments and backgrounds. One of the key findings is that the quality and diversity of the dataset play a crucial role in determining the performance of the model.

1. Wei, X.-S., Song, Y.-Z., Mac Aodha, O., Wu, J., Peng, Y., Tang, J., Yang, J., Belongie, S. “Fine-Grained Image Analysis with Deep Learning: A Survey.” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, No. 12, pp. 8927–8948, Dec. 2022. Nanjing University Computer Science 2. Fiorucci, M., Khoroshiltseva, M., Pontil, M., Traviglia, A., Del Bue, A., James, S. “Machine Learning for Cultural Heritage: A Survey.” Pattern Recognition Letters, Vol. 133, pp. 102–108, 2020. Durham Research Online 3. Figueira, A., Vaz, B. “Survey on Synthetic Data Generation, Evaluation Methods and GANs.” Mathematics, Vol. 10, No. 15, p. 2733, 2022. (MDPI) MDPI 4. Chakraborty, T., Reddy, U. R. K. S., Naik, S. M., Panja, M., Manvitha, B. “Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art.” arXiv preprint arXiv:2308.16316, 2023. (comprehensive GAN survey; arXiv). arXiv 5. Wang, Y., Yao, Q., Kwok, J. T., Ni, L. M. “Generalizing from a Few Examples: A Survey on Few-Shot Learning.” ACM Computing Surveys, Vol. 53, No. 3, Article 63 (pages 1–34), 2020. (ACM) ResearchGate 6. Khan, A., Sohail, A., Shah, Z. A., et al. “A Survey on Vision Transformers.” Journal / Survey article (see ACM / IEEE special issue collections), 2022. (Use for transformer/attention background relevant to attention layers in coin models). 7. Iglesias, G., et al. “A survey on GANs for computer vision: recent research, challenges and opportunities.” *(Computer-vision GAN survey, 2023 — journal article / review). * (Good background for using GANs to augment worn coin images). 8. Han, M., et al. “Joint Banknote Recognition and Counterfeit Detection via Explainable AI.” Sensors, Vol. 19, No. 16, Article 3607, 2019. (Example currency recognition survey / methods paper). 9. Jozdani, S., et al. “A review and meta-analysis of Generative Adversarial Networks.” Neurocomputing / Elsevier (review), 2022. (meta-analysis of GANs and applications — useful for synthetic augmentation literature). 10. Wei, X.-S. et al. (extended resources) “Fine-Grained Image Analysis: datasets, code, and benchmarks.” Companion resources / extended survey materials (IEEE TPAMI companion), 2022. (Useful for coin motif / fine-grained references). Nanjing University Computer Science 11. Survey on Coin Detection Using Deep Learning — IJRPR (International Journal of Research in PR) / 2022–2023. “A Survey on Coin Detection Using Deep Learning.” (recent coin-specific review summarizing DL detection/classification papers and datasets). 12. “Digital Restoration of Cultural Heritage — A Survey” (author(s) various) Journal / conference survey (2023) — surveys data-driven restoration, inpainting & 3D reconstruction techniques (helpful for worn coin restoration). 13. Survey on Synthetic Data Generation: evaluation & practices (additional) (complementary surveys exploring synthetic data evaluation metrics — MDPI + arXiv 2022–2024). 14. Surveys on Object Detection in Visual Art & Cultural Items (2023) “Object Detection in Visual Art: A Survey” — discusses adaptation of general detectors (YOLO/SSD/RCNN families) to cultural imagery; useful for coin localization/detection pipelines. 15. Survey: Few-Shot & Incremental Learning (2022–23 compendia) “A Comprehensive Survey of Few-Shot Learning” — ACM / Elsevier surveys (2022–2023) that synthesize approaches that are practical where datasets are small (high relevance to rare coin classes). [Citation Format]

Submit Your Paper Call for Papers About IJORET Journal Scope Review Process