The concept of self-service retail store has been successfully adopted around the world since its inception in 1916 United States of America. People carry their required retail products in a cart, hand-basket and make their purchase by standing in the queue to make payment. With advancements in electronics and computer sciences, this project aims to ease the “traditional purchase experience” at the same time maintaining the conventional “Hold-Observe-Purchase” experience by attempting to automate the billing procedure. Using deep learning algorithms and Image processing principles, the product images are recognized and its pertaining information is used to generate the transactional ledger as the products are added. This phase of the project aims at training our model to recognize custom images of Indian Retail products more specifically “Indian Consumable Retail products”. Furthermore, the trained model is deployed on a “Single Board Computer (SBC)” such as “Raspberry Pi 4” to recognize the product images by taking its picture with the Raspberry Pi camera attached to the SBC and running the image through the trained model to identify it and generate the transaction ledger.
Objectives: Primary objective to develop this project, was to eliminate the need of long payment queues especially in weekends in retail stores by automating the payment procedure. Implementing a complex principle of image recognition to promote “Digital payment revolution in India under atmanirbhar bharat” as predicted results are used to generate the transactional ledger. To learn and create a custom image data set of the retail products using data augmentation and image processing principles which is comprised of internet search results and photos captured by smart phone. To deploy a pre-trained image recognition algorithm (trained on Laptop computer) on a SBC such as raspberry pi attached with a camera which can capture image and can conveniently execute the pre trained model bypassing the need of advanced computation power.
Inhaltsverzeichnis (Table of Contents)
- A Deep Learning Based Spontaneous Retail Product Identification
- 1.1 Deep Learning
- 1.2 Perceptron
- 1.3 Convolutional Neural Networks (CNN)
- 1.3.1 Convolutional Neural Network Layers
- 1.4 The Tools
- 1.5 Creating the training data set
- 1.5.1 Importance of the dataset
- 1.6 Images
- 1.7 Data Augmentation
- 1.8 Canny Edge Detection
- 1.9 Data Extraction
- 1.10 Training the model
- 10.1.1 Preparing the data for training
- 1.11 The CNN Model
- 1.12 Trained model output
- 1.13 Output
- 1.14 Hardware flow diagram
- 1.15 Raspberry Pi
- 1.15.1 Raspberry pi 4 mode B 4GB
- 1.16 Raspberry Pi setup
- 1.17 Raspberry PI output
- 1.18 Test results merits and demerits
- 1.19 Conclusion & future enhancements
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
The primary objective of this project is to automate the billing procedure in retail stores, thereby eliminating long payment queues, particularly during peak hours. This is achieved through the implementation of a deep learning-based image recognition system for Indian consumable retail products. The project also aims to contribute to the digital payment revolution in India and explores the creation and utilization of a custom image dataset for training the model. Finally, it investigates the deployment of a pre-trained model on a low-power Single Board Computer (SBC) like a Raspberry Pi.
- Automation of retail checkout processes using image recognition.
- Development and application of a custom deep learning model for Indian retail products.
- Deployment of a deep learning model on a low-power Single Board Computer.
- Creation and augmentation of a custom image dataset for training.
- Contribution to the digital payment revolution in India.
Zusammenfassung der Kapitel (Chapter Summaries)
A Deep Learning Based Spontaneous Retail Product Identification: This introductory chapter presents the project's core concept: automating the retail checkout experience using deep learning and image recognition. It outlines the primary objective of eliminating long queues, the use of deep learning algorithms and image processing for product identification, and the deployment of the trained model on a Raspberry Pi. The chapter also briefly touches upon data augmentation techniques used to create a sufficient training dataset of Indian retail products.
1.1 Deep Learning: This section introduces the fundamental concepts of deep learning, describing it as a subfield of machine learning that mimics the structure and function of the human brain. It explains supervised learning techniques, using the biological neuron as an analogy, laying groundwork for understanding the perceptron algorithm.
1.2 Perceptron: This section details the perceptron, a fundamental deep learning algorithm, comparing its structure to that of a biological neuron and explaining how it uses input, weights, and bias to classify data. It outlines the process of updating parameters to improve prediction accuracy through iterative training and the underlying mathematical function involved in this process.
1.3 Convolutional Neural Networks (CNN): This section introduces Convolutional Neural Networks (CNNs), a more advanced deep learning architecture than the perceptron. The architecture is described in terms of its input layer, output layer, and various hidden layers (convolutional, pooling, and ReLU correction layers), each with their respective functions and importance in image recognition. This lays the foundation for understanding the model architecture used in the subsequent phases of the project.
1.4 The Tools: This section details the software tools used in the project, including OpenCV for computer vision tasks, Pandas for data manipulation, Keras as a deep learning API, and TensorFlow as the backend engine. This provides context for the implementation details discussed later in the document.
1.5 Creating the training data set: This chapter emphasizes the critical role of data in machine learning, highlighting the need for a custom dataset specifically tailored for Indian retail products. It discusses the challenges of obtaining and preparing suitable data for training a deep learning model for accurate product recognition. The importance of sufficient data and the methods used to overcome its limitations are explored.
1.7 Data Augmentation: This chapter delves into data augmentation techniques used to expand the custom image dataset. It details how image transformations, such as rotation, shifting, shearing, and zooming, were applied to increase the volume and diversity of the training data, ultimately improving the model’s robustness and generalization capabilities.
1.8 Canny Edge Detection: This chapter describes the use of Canny edge detection as a pre-processing step to enhance image features. It explains how this technique helps to highlight the texture and edges in images of retail products, improving the model's ability to distinguish between similar-looking items and overcome challenges related to the visual complexity of Indian retail products.
1.9 Data Extraction: This section details the process of extracting pixel values from the augmented images and organizing them into a structured dataset suitable for training the CNN model. It describes the workflow of reading images, converting them to grayscale, resizing, applying the Canny edge detection filter, and storing the processed data in an efficient format for subsequent use.
1.10 Training the model: This section covers the preparation of the dataset for training and explains the process of training the CNN model. It includes steps such as separating labels from image data, converting labels into a suitable categorical format, and reshaping the image data into a numpy array for input into the CNN. The training process, using parameters like epochs and batch size, is described.
1.11 The CNN Model: This chapter describes the architecture of the custom CNN model used for retail product identification. It outlines the layers of the network, including convolutional layers, pooling layers, dropout layers, and fully connected layers, explaining the function and purpose of each component. It also describes the activation functions (ReLU and softmax) and the compilation process of the model.
1.12 Trained model output: This section details the implementation and testing of the trained CNN model, describing how the model processes input images and generates predictions. The steps involved in image processing, input transformation, and model execution are outlined, leading to the generation of a transactional ledger based on the identified products.
1.15 Raspberry Pi: This section introduces the Raspberry Pi 4 as the chosen SBC for deploying the trained model. It describes the hardware specifications and operating system capabilities of the device, emphasizing its suitability for resource-constrained deployments of machine learning models.
1.16 Raspberry Pi setup: This chapter explains the setup and configuration of the Raspberry Pi for image capture and model execution. It describes the use of the Raspberry Pi camera and provides the command used for capturing images, emphasizing the simplicity of integration.
1.17 Raspberry PI output: This section presents the results obtained from running the trained model on the Raspberry Pi, demonstrating the functionality of the system in a real-world scenario.
1.18 Test results merits and demerits: This section analyzes the performance of the deployed model, identifying its strengths and weaknesses. It discusses the accuracy of the model under various conditions and potential areas for improvement, providing valuable insights into the limitations of the approach.
Schlüsselwörter (Keywords)
Deep learning, Convolutional Neural Networks (CNN), image recognition, retail product identification, Indian retail products, data augmentation, Canny edge detection, Raspberry Pi, Single Board Computer (SBC), digital payment revolution, object recognition, computer vision, supervised learning, data preprocessing, model deployment, IoT, Atmanirbhar Bharat.
A Deep Learning Based Spontaneous Retail Product Identification: FAQ
What is the main objective of this project?
The primary goal is to automate the billing process in retail stores, reducing long queues, especially during peak hours. This is accomplished using a deep learning-based image recognition system for Indian consumable retail products.
What technology is used in this project?
The project utilizes deep learning, specifically Convolutional Neural Networks (CNNs), for image recognition. It employs tools like OpenCV, Pandas, Keras, and TensorFlow. The trained model is deployed on a Raspberry Pi 4.
What is the role of the image dataset?
A custom dataset of Indian retail products is crucial. Data augmentation techniques (rotation, shifting, etc.) are used to increase the dataset's size and diversity, improving model accuracy and robustness.
How does the system work?
The system captures images of retail products. The CNN model processes these images, identifying the products, and generates a transactional ledger. This automated identification speeds up the checkout process.
What are the key stages of the project?
The project involves creating a training dataset, implementing data augmentation and Canny edge detection, training a CNN model, deploying the model on a Raspberry Pi, and testing its performance.
What are the challenges addressed in this project?
Challenges include creating a sufficient and representative dataset of Indian retail products, dealing with the visual complexity of these products, and deploying a deep learning model on a resource-constrained device like a Raspberry Pi.
What are the results and future enhancements?
The project presents test results, analyzing the model's performance, accuracy, and potential areas for improvement. Future enhancements could focus on improving accuracy, expanding the product database, and refining the system for real-world retail settings.
What is the significance of using a Raspberry Pi?
The Raspberry Pi serves as a low-power Single Board Computer (SBC) for deploying the trained model, making the solution cost-effective and suitable for resource-constrained environments.
What are the key algorithms and techniques used?
Key algorithms and techniques include deep learning, CNNs, image recognition, data augmentation, Canny edge detection, and supervised learning. The project also leverages image processing and data preprocessing techniques.
What is the contribution to the digital payment revolution in India?
This project aims to contribute to India's digital payment revolution by automating the checkout process, which can improve efficiency and reduce reliance on cash transactions.
- Arbeit zitieren
- Dr. Upesh Patel (Autor:in), Sachi Joshi (Autor:in), 2023, A Deep Learning Based Spontaneous Retail Product Identification, München, GRIN Verlag, https://www.hausarbeiten.de/document/1416457
