Coin Recognition using Artificial Neural Network

Problem Definition

Problem Description: One of the challenges faced in the coin recognition system is the frequent machine cleaning required for dirty coins. Additionally, the variations in images obtained between new and old coins pose a problem in accurate recognition. The current process involves several steps such as acquiring RGB coin image, generating pattern averaged image, removing shadow from the image, cropping and trimming the image, converting RGB image to grayscale, generating feature vector, and passing it as input to a trained artificial neural network (ANN) to give appropriate results based on the output of the NN. However, as the problem becomes more complex and large-scale, the number of operations increases, making hardware implementation difficult. This project aims to address these challenges by designing a small-sized neural network system to reduce costs and simplify hardware implementation for real coin recognition systems.

Proposed Work

The proposed work aims to design an Artificial Neural Network (ANN) for a coin recognition system. The project focuses on addressing the challenges posed by dirty coins and the variations in images of new and old coins. The coin recognition process is broken down into seven steps, including acquiring RGB coin images, removing shadows, and converting images to grayscale. The proposed method involves designing a neural network for coin recognition, with a goal of simplifying hardware implementation and reducing costs. By utilizing modules such as Regulated Power Supply and Rain/Water Sensor, along with MATLAB GUI, the system aims to achieve efficient coin recognition through image processing and computer vision techniques.

This research work falls under the categories of Image Processing & Computer Vision, M.Tech | PhD Thesis Research Work, MATLAB Based Projects, and Optimization & Soft Computing Techniques, with subcategories including Image Classification, Image Recognition, MATLAB Projects Software, and Neural Network.

Application Area for Industry

This project can be widely used in various industrial sectors such as banking, retail, vending machine industries, and coin-operated machines. In the banking sector, the coin recognition system can help in accurate sorting and counting of coins, reducing errors and improving efficiency. In retail, this system can be used in self-checkout machines to accurately identify and process different denominations of coins. Vending machine industries can benefit from this project by ensuring that the correct change is given to customers. Additionally, in coin-operated machines such as laundromats or arcade games, this system can help in validating and processing coins accurately.

By implementing the proposed solutions in these industrial domains, the challenges posed by dirty coins and variations in coin images can be effectively addressed. The use of a small-sized neural network system can reduce costs and simplify hardware implementation, making it a practical and efficient solution for real coin recognition systems. Overall, the benefits of implementing this project include improved accuracy in coin recognition, increased efficiency in coin processing, and cost reduction in hardware implementation.

Application Area for Academics

The proposed project of designing an Artificial Neural Network (ANN) for a coin recognition system offers significant potential for research by MTech and PhD students in various fields. This project addresses the challenges faced in coin recognition systems, specifically focusing on issues related to dirty coins and variations in images of new and old coins. By breaking down the coin recognition process into several steps and utilizing modules such as Regulated Power Supply and Rain/Water Sensor, along with a MATLAB GUI, this research work presents a unique opportunity for students to explore innovative research methods in the fields of Image Processing & Computer Vision, MATLAB Based Projects, and Optimization & Soft Computing Techniques. MTech and PhD students can use the proposed project for their dissertation, thesis, or research papers by leveraging its relevance in developing advanced image processing techniques, exploring neural network models for efficient coin recognition, and implementing computer vision algorithms for real-world applications. The project's focus on simplifying hardware implementation and reducing costs makes it particularly valuable for researchers looking to optimize and enhance existing coin recognition systems.

The code and literature of this project can serve as a valuable resource for field-specific researchers, MTech students, and PhD scholars interested in Image Classification, Image Recognition, MATLAB Projects Software, and Neural Network research domains. Furthermore, this project opens up avenues for future research in exploring new methods for enhancing coin recognition accuracy, developing autonomous coin recognition systems, and integrating machine learning algorithms for more robust performance. The potential applications of this research work extend beyond coin recognition systems to various other domains requiring image processing and computer vision technologies. Overall, this project offers a promising platform for MTech and PhD students to pursue innovative research methods, conduct simulations, and analyze data for their academic endeavors, with a reference to future scope in advancing the field of coin recognition and related research areas.

Keywords

coin recognition system, dirty coins, RGB coin image, shadow removal, grayscale conversion, neural network system, hardware implementation, artificial neural network, image processing, computer vision, Regulated Power Supply, Rain/Water Sensor, MATLAB GUI, efficient coin recognition, Image Classification, Image Recognition, MATLAB Projects Software, Neural Network, neurofuzzy, classifier, SVM, decision making, optimization, soft computing techniques, image acquisition, matching, Linpack.