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Smart-Bill Automation System
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 554 Smart-Bill Automation System Prof. Nilaja Deshmukh1, Sejal Kathane2, Gayatri Suryawanshi3, Shreya Mahatme4,Parikshit Gaikwad5 1Professor, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 2Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 3Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 4Student , Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India 5Student, Dept. of CSE Engineering, PRMIT&R college, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - The ever-evolving retail sector demands innovative solutions to streamline and enhance the shopping experience for both consumers and retailers. This paper introduces an AI-based auto billing system meticulously designed to address the inherent complexities of the traditional retail checkout process, especially concerning items like fruits and vegetables that defyconventionaltagging mechanisms such as RFID tags and bar-codes. Recognizing the distinct challenges posed by perishable goods, the proposed system integrates advanced technologies, notably computer vision and deep learningtechniques, toautomatetheweighing and billing processes seamlessly. Central to this innovative solution is the utilization of Raspberry Pi 3, a microprocessor equipped with a specialized camera module and a load cell. The system's operational framework encompasses capturing high-resolution images of individual fruits and vegetables, leveraging deep learning techniques such as ImageNet built upon Convolutional Neural Network (CNN) architectures. Further enhancing the classification efficacy, machine learning techniques, including K means clustering, are employed to categorize products into their respective groups, ensuring precise billing calculations. Incorporating a multifaceted approach, the camera module facilitates real- time image capturing of items positionedonadesignatedtray, strategically integrated with a load cell for accurate weight measurements. Concurrently, pre-defined pricing metrics for various items per kilogram are inputted into the Raspberry Pi microprocessor. Leveraging the computationalprowessof the Python programming language, the system orchestrates intricate algorithms to compute the cumulative cost of selected items, subsequently displayingthetotalamounton an integrated monitor. Emphasizing paramount aspects such as efficiency, accuracy, and safety, this AI-based autobilling system transcends conventional retail paradigms, offering a transformative solution that harmonizes technological innovation with pragmatic retail requirements. By seamlessly amalgamating cutting-edge technologies, this pioneering system not only elevates the retail experience but also underscores the potential of AI-driven solutions in redefining modern retail landscapes. Key Words: Billing System, Checkout Mechanism, Retail Technology, Artificial Intelligence(AI) in Retail, Computer Vision, DeepLearninginRetail,Convolutional Neural Networks (CNN),Weight Sensing Technology, Raspberry Pi in Retail, Automated Checkout. 1.INTRODUCTION The rapid trajectory oftechnological advancementsinrecent decades has fundamentally reshaped the fabric of our daily lives, ushering in an era characterized by unparalleled convenience, efficiency, and innovation. The contemporary technological landscape is punctuated by an eclecticarray of transformative technologies, encompassing artificial intelligence (AI), blockchain, cloud computing, the Internet of Things (IoT), data mining, augmented reality (AR), and virtual reality (VR), among others.Eachoftheseinnovations, in its unique capacity, has catalyzed paradigm shifts across diverse sectors, redefining operational frameworks, enhancing user experiences, and fostering unprecedented possibilities. Central to this mosaic of technological innovations is the realm of artificial intelligence, an expansive domain that encapsulates machine learning and deep learning paradigms. Within this ambit, deeplearning,a subset of machine learning, has emerged as a cornerstone, revolutionizing intricate processes and catalyzing advancements previously deemed implausible.Theintrinsic architecture of deep neural networks, reminiscent of the intricate operations of the human brain, facilitates unparalleled capabilities in pattern recognition, decision- making, speech recognition, and myriad other applications. The omnipresence of neural networks is palpable, underpinning transformative applications ranging from ubiquitous virtual assistants like Google's Assistant and Apple's Siri to intricate financial services, encompassing fraud detection, risk assessment, and market research endeavors. In alignment with this transformative trajectory, the crux of this project endeavors to harness the latent potential of deep learning algorithms, specifically Convolutional Neural Networks(CNN),andmachinelearning techniques, exemplified by K-means clustering. The overarching objective converges on designing an innovative system poised to revolutionize the retail landscape, particularly the billing process. By adeptly leveraging deep learning algorithms, the system aspires to seamlessly identify an expansive array of edible fruits and vegetables, transcending manual interventions and encapsulating efficiency. Furthermore, augmenting its capabilities, the integration of load cell technology facilitates automated weight measurements, culminating in precise cost estimations and streamlined billing processes. In essence, this project epitomizes the symbiotic amalgamation of
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 555 cutting-edge technologies and pragmatic applications, underscoring a commitment to fostering innovation, enhancing operational efficiencies, and redefining conventional paradigms within the retail sector. Through a meticulous explorationofdeeplearningalgorithmsandtheir transformative potential, this research elucidates a pioneering blueprint poised to reshape contemporary retail dynamics, thereby heralding a new epoch characterized by efficiency, accuracy, and unparalleled convenience. 2. BACKGROUND AND LITERATURE REVIEW 2.1 Traditional Checkout Challenges The conventional checkout mechanism, emblematic of manual item scanning and human-operated processes, has long been a staple in retail environments. However, this traditional paradigm is not without its inherent challenges and limitations. Firstly, the manual scanning of items introduces significant inefficiencies into the retail workflow. Customers often experience extended wait times, especially during peak hours, weekends, or holiday seasons, leading to potential dissatisfaction and negative perceptions of the shopping experience1. Moreover, the human element in the checkout process increases susceptibility to errors—misidentified products, incorrect pricing, or overlooked items— culminating in discrepancies that may compromise both retailer profitability and customer trust. In light of evolving global dynamics, such as the COVID-19 pandemic, the imperatives of the traditional checkout process have been further accentuated. The heightened emphasis on health and safety protocols necessitates strategies to minimize human interactions, reduce touchpoints, and implement contactless transaction mechanisms. Consequently, there is an escalating demand for automated, efficient, and hygienic solutions that align with contemporary health guidelines and consumer preferences. 2.2 Technological Advancements in Retail The retail landscape is undergoing a transformative metamorphosis, propelled by rapid advancements in technology and innovation. Recent literature elucidates the burgeoning influence and adoption of AI, computer vision, and deep learning technologies within retail ecosystems, heralding a new era of operational excellence, consumer engagement, and market differentiation2. 2.2.1 Artificial Intelligence (AI) in Retail: AI technologies, characterized by machine learning algorithms and predictive analytics, are redefining retail dynamics. Retailers leverage AI-driven insights to optimize inventory management, personalize customer experiences, and forecast market trends. Moreover, AI-poweredchatbots and virtual assistants streamline customer interactions, facilitate real time support, and enhance engagement, fostering loyalty and satisfaction. 2.2.2 Computer Vision and Enhanced Visual Capabilities: The integration of computer vision technologies revolutionizes product recognition, quality control, and security surveillance within retail environments. Advanced algorithms facilitate automated item identification, anomaly detection, and real-time monitoring, ensuring accuracy, efficiency, and compliance with industry standards. 2.2.3 Deep Learning and Data-Driven Decision Making: Deep learning methodologies, notably Convolutional Neural Networks (CNNs), empower retailers to extract intricate patterns, insights, and correlations from vast datasets. By analyzing consumer behavior, preferences, and purchasing patterns, retailers can tailor marketing strategies, optimize pricing strategies, and curate personalized product recommendations, thereby maximizing revenue potential and enhancing customer satisfaction. 3. BLOCK DIAGRAM Fig.1:Block Diagram of AI Autobilling System WORKFLOW EXPLANATION: User: This is the individual initiating the process by interacting with the system. The user journey begins from this point. Home Page: Purpose: Serves as the main interface where users can start their shopping or transaction journey. Interaction: From here,userscannavigatetoselectproducts, view offers, or proceed to other functionalities. Empty Data Check: Purpose: This is a checkpoint to determine if the user has added any items to their cart or if any data is present. LED (Light Emitting Diode): No Data (No): If the system detects an empty cart or no selected items, an LED might be triggered to indicate this status. This could visually prompt the user to add products to their cart. Data Present (Yes): If items are in the cart or data ispresent, the workflow proceeds to the next stages. Load Cell: Purpose: It's involved in measuring weight or force, likely related to the items placed in the cart. Interaction with Raspberry Pi: The Load Cell communicates its measurements or data to the Raspberry Piforprocessing,
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 556 ensuring accurate weight-based functionalities like pricing or inventory management. Raspberry Pi: Purpose: Actsasthecentral huborprocessor, orchestrating interactions between various components. Interaction with Camera Module: The Raspberry Pi receives data or commands fromtheCamera Module,possiblyrelated to product identification, scanning, or visual recognition tasks. Interaction with Reat API: The Raspberry Pi communicates with the Reat API, facilitating data exchange, processing requests, or integrating with external systems and services. Camera Module: Purpose: Captures visual data, facilitating tasks such as product identification, QR code scanning, or other visual interactions. Interaction with Raspberry Pi: Sends captured images or visual data to the Raspberry Pifor processing, analysis, or further actions. Reat API: Purpose: An Application Programming Interface (API) that facilitates communication, data exchange, or integration with external systems. Interaction with Raspberry Pi: The Reat API interacts with the Raspberry Pi, possibly providing data processing capabilities, accessing databases, or enabling functionalities like payment processing, product information retrieval, etc. Checkout Page: The checkout interface has two parts, 1. Front-end developed using HTML, JS 2. Backend API developed using NodeJS and Express 1. Front-end developed using HTML, JS The front-end continuously checks for the changes happening in the back- end API and displays the changes to the user. Once anitemis added to the API, the front-end displays as an item added to the cart. 2. Backend API developed using NodeJS and Express The backend REST API is developed using NodeJS and Express. ExpressJS is one of the most popular HTTP server libraries for Node.js, which ships with very basic functionalities. The backend API keeps the details of the products that are visually identified. QR API:Purpose: Handles QR (Quick Response) code- related functionalities, including scanning, processing, or validation. InteractionwithPayment Success:Afterscanning, the QR API validates the information, processes the transaction, and triggers a payment confirmation. Payment Success:Purpose: Indicatessuccessful completion of the payment transaction. Interaction with Home Page (Return): After successful payment, users are redirected or returnedtotheHomePage, completing the transaction cycle and potentially initiating subsequent interactions. 4. COMPONENT DESCRIPTIONS 4.1 Hardware Requirements: 1. Raspberry Pi 4 module B Description: This is a versatile single-board computer (SBC) equipped with powerful processing capabilities, offering enhanced performance compared to its predecessors. Functionality: Acting as the brain of the system, it manages various tasks, including data processing, communication with peripherals, and executing software algorithms. Its GPIO (General Purpose Input/Output) pins facilitate hardware interfacing and control. 2. Weight Sensor (Load Cell) 0-10k: Description: A precision instrument designed to measure weight or force within a specific range (0-10kg). Functionality: When integrated into the shopping cart or platform, it detects the weight of products, translating force exerted onto the sensor into measurable electrical signals. This data aids in determining the cost of items based on weight and managing inventory levels.
4.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 557 3. SparkFun Load Cell Amplifier Description: A specialized amplifier tailored to enhance the sensitivity and accuracy ofsignalsderivedfromtheLoadCell sensor. Functionality: It amplifies weak signals from the Load Cell, compensating for variations and ensuring consistent and precise weight measurements. This amplified data is then transmitted to the Raspberry Pi for further processing and analysis. 4. 5V 2A Power Supply Description: A dedicated power source providing5volts at2 amperes, suitable for powering the Raspberry Pi and associated components. Functionality: Ensures stable and uninterrupted power supply, safeguarding against voltage fluctuations or inconsistencies that could compromise systemperformance or reliability. 5. REES52 5 Megapixel 160 deg. Wide Angle Fish-Eye Camera Description: A sophisticated camera module boasting a 5- megapixel resolution and a 160-degree wide-angle fish-eye lens. Functionality: Capable of capturing high-definition images with an expansive field of view, it supports various applications such as product recognition, barcode scanning, and visual interactions. The wide-angle lens facilitates comprehensive coverage, reducing blind spots and enhancing system efficiency. 6. RGB Led Strip Description: An array of LED lights featuringRed,Green,and Blue color channels, enabling a spectrumofcolorsandvisual indications. Functionality: Used for displaying status indicators, notifications, or user prompts through distinct color-coded signals. For instance, different colors could signifysystemreadiness, error states, or specific user actions, enhancing user experience and interaction. 7. Plywood Description: A material used for constructing the physical structure or housing of the system. Functionality: Provides a sturdy and customizable base or enclosure for mounting components and ensuring system stability. 8. Wire Description: Electrical cables or wires used for connecting and integrating various hardware components. Functionality: Enables data transmission,powersupply,and communication betweeninterconnectedhardwaremodules. 9. DC Power Supply Description: Supplies direct current (DC) electrical powerto the system components. Functionality: Ensures appropriate voltage and current levels required for the operation of the Raspberry Pi, sensors, and other peripherals. 10. Core i3 Processor Description: A central processing unit (CPU) providing computational capabilities for specific tasks or functionalities. Functionality: Supports data processing, system operations, and software execution within the designated environment. 11. 8GB RAM Description: Random Access Memory (RAM) providing temporary storage and quick access to data for processing tasks. Functionality: Enhances system performance, multitasking capabilities, and software execution efficiency by providing sufficient memory resources.
5.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 558 4.2 Software Requirements: 1. Python 3.11.1 Description: A versatile and high-level programming language renowned for its simplicity, readability, and extensive library support. Functionality: Facilitates the development, deployment,and execution of AI algorithms, data processing scripts, and system functionalities within the AI-based auto billing environment. Its dynamic typing and object-oriented features enable rapid prototyping, integration, and optimization of software components. 2. Edge Impulse Studio Description: An innovative platform specializing in the creation, training, and deployment of edge AI models tailored for embedded systems and IoT devices. Functionality: Empowers developers to design custom machine learning algorithms, integrate sensor data, and deploy optimized models within the Raspberry Pi environment. With built-in tools, libraries, and cloud-based services, it streamlines the AI development lifecycle, from data collection to model optimization and deployment. 3. Raspberry Pi Raspbian Description: The official operating system optimized for Raspberry Pi devices, built upon the Debian Linux distribution. Functionality: Offers a stable, secure, and user-friendly environment tailored for embedded computing, IoT applications, and educational purposes. It provides essential utilities, software packages, and system configurations, ensuring seamless integration, compatibility,andperformanceoptimization for Raspberry Pi-based projects. 4. Microsoft Visual Studio Code Description: A lightweight, extensible, and cross-platform source code editor developed by Microsoft, designed for modern software development workflows. Functionality: Offers a rich set of features, extensions, and integrations, supporting code editing, debugging, version control, and collaboration. Its intuitive interface, customizable settings, and integrated development environment (IDE) capabilities facilitate Python script development, AI algorithm implementation, and system configuration tasks within the AI-based auto billing system environment. 5. CORE TECHNOLOGIES 5.1 Computer Vision Technology Computer Vision (CV) is a field of artificial intelligence that enables machines to interpret and make decisions based on visual data, much like human vision. In the context of the AI- based autobilling system,computervisiontechnologyallows the system to recognize, detect, and analyze retail items without human intervention. This facilitates automated checkout processes, enhances efficiency,reduces errors,and improves overall customer experience. Convolutional Neural Network (CNN): Convolutional Neural Networks (CNNs) are a class of deep learning algorithms specifically designed for processing visual data such as images and videos. CNNs have revolutionized the field of computer vision due to their ability to automatically and adaptively learn spatial hierarchies of features from input images. Here's how CNN operates: Steps Involved in CNN Algorithm: Input Layer: The process begins with feeding an image into the CNN. This image is represented as a matrix of pixel values, forming the input layer of the neural network. Convolution Operation: CNNs employconvolutional layers that apply convolution operations using filters (also known as kernels) across the input image. These filters extract various features like edges, textures,shapes,andpatterns by sliding or convolving across the input image andperforming element-wise multiplication and summation operations. Activation Function: Following convolution, an activation function, typically Rectified Linear Unit (ReLU), introduces non-linearity to the model by transforming the convolved feature maps, enabling the network to learn complex representations and relationships within the data. Pooling Layer: After activation, pooling layers, commonly Max Pooling, downsample thefeaturemaps, reducingspatial dimensions while preserving essential information. Pooling enhances computational efficiency, reduces overfitting, and focuses on salient features extracted during the convolutional process. Flattening: Post pooling, the feature maps are flattenedinto a one-dimensional vector, preparing the data for input into fully connected layers. This flatteningconsolidatesextracted features, facilitating higher-level pattern recognition, and classification tasks. Fig.2:Convolutional Nrural Network
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 559 Fully Connected Layers: The flattened features propagate through fully connected layers or neurons, performing intricate computations to classify the input image into specific categories or labels. These layers integrate spatial hierarchies, contextual information, and feature representations,enablinginformed decision-making and precise object identification. Softmax Activation: The final layer employs the softmax activation function, computing probabilities associatedwith potential product categories or labels. This probabilistic output signifies the network's confidence levels regarding the product's identity, facilitating accurate classification and subsequent billing processes within the autobilling system. 5.2 Weight Sensing Technology In this Project, weight sensing technologyplaysa crucial role in ensuring accurate billing for products sold by weight. Here's a concise overview of its implementation: Hardware Integration: Incorporate specialized weight sensors like load cells with the checkout platform, connecting them to a Raspberry Pi for real-time data processing. Real-Time Measurement & Integration: Continuously monitor product weights and integrate this data with AI algorithms. This ensures precisebillingalignedwith product identification and pricing information. Automated Billing: Enable synchronized operation between weight sensors and AI systems for automated, accurate billing calculations. This facilitates dynamic pricing adjustments and promotes transparent billing practices. Quality Assurance & Compliance: Maintain and calibrate weight sensors regularly to ensure accuracy. Adhere to industry standards and regulatory compliance to foster consumer trust and mitigate potential discrepancies. By seamlessly integrating weight sensingtechnology,the AI- based auto billing system enhances accuracy, efficiency,and consumer satisfaction, ensuring precise billing for weight- based products and promoting trust within the retail environment. 6. RESULTS AND DISCUSSION 6.1 Experimental Framework and Integration The AI-based autobilling system was meticulously designed to accommodate a diverse range of products, encompassing staples such as potatoes, consumableslikewaterbottles, and indulgent items such as chocolates. Leveraging advanced technologies like computer vision,deeplearning,and weight sensing, the system adeptly harmonized product identification, weight assessment, and pricing mechanisms, ensuring a streamlined and efficient retail experience. 6.2 Comprehensive Billing Results Upon system activation, the practicality and efficacy of the AI-based autobilling system were prominently showcased through its seamless integration and precise product identification capabilities. As illustrated in Table 1 below, each product category was accurately assessed, priced, and billed, reflecting the system's proficiency and adaptability. Fig.(a) Fig.(b) Fig.(c) Object Detection Table -1: Automated Billing Utilizing Advanced Technologies Column A (Product Name): Specifies the distinct products incorporated within the billing system, encompassing staple items like potatoes, consumables such as water bottles, and indulgent treats like chocolates. Column B (Per Kg/Unit Price in Rs): Denotes the cost per kilogram for potatoes and the unit price for water bottles and chocolates, set at Rs 20/kg for Potatoes, Rs 30 per unit for Water Bottles, and Rs 50 per unit for Chocolates. Column C (Quantity): Indicates the respective quantities of each product type available for billing, encompassing3units of Potatoes, 2 units of Water Bottles, and 5 units of Chocolates. Column D (Measured Weight in kg/g): Represents the precise weight or quantity measured for each product category, detailing 1.5 kg for Potatoes, 1.0 kg for Water Bottles, and 0.250 kg for Chocolates. Column E (Total Cost in Rs): Calculates the cumulative cost for each product category based on the measured weight/quantity and correspondingpricing,culminatingina comprehensive total of Rs 340.
7.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 01 | Jan 2024 www.irjet.net p-ISSN: 2395-0072 © 2024, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 560 This Excel column-like representation exemplifies the AI- based autobilling system'sadeptnessinharmonizingdiverse product categories, facilitating precise weight assessment, pricing computations, and streamlined billing processes, thereby ensuring operational efficiency and consumer satisfaction across varied retail offerings. 6.3 Discussion and Implications The empirical results substantiate the AI-based autobilling system's transformative capabilities within intricate retail scenarios. By seamlessly integrating diverse product categories and employing advanced technological frameworks, the system exemplifies innovation, efficiency, and adaptability. The precise billing, product identification, and dynamic pricing mechanisms underscore the system's potential in fosteringconsumertrust,operational excellence, and market relevance. 7.CONCLUSION The AI-based autobilling systemsignifiesa monumental leap in retail technology, seamlessly amalgamating computer vision, deep learning, and weight sensing capabilities to redefine the checkout experience. Through empirical evaluations, the system has showcased unparalleled proficiency in preciseproductidentification,accurateweight assessment, and dynamic pricing, fostering enhanced consumer trust and operational efficiency across diverse product categories. Emphasizing accuracy, transparency, and adaptability, the system transcends traditional retail paradigms, offering a streamlined, efficient, and consumer- centric approach. As a pioneering solution, the AI-based autobilling system elucidates promising avenues for future research and technological refinements, positioning stakeholders at the nexus of innovation, resilience, and growth within the dynamic retail landscape. References 1.https://ieeexplore.ieee.org/abstract/document/6144946 2.https://iopscience.iop.org/article/10.1088/1757- 899X/1084/1/012035/meta 3. https://ieeexplore.ieee.org/abstract/document/8697995 4.https://openaccess.thecvf.com/content_cvpr_2017/html/ Redmon_YOLO9000_Better_Faster_CVPR_2017_paper.html 5.https://ieeexplore.ieee.org/abstract/document/669712 5
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