In a groundbreaking collaboration, Dr Banee Bandana Das, Assistant Professor in the Department of Computer Science and Engineering, and Dr Saswat Kumar Ram, Assistant Professor in the Department of Electronics and Communication Engineering, have joined forces with Btech-CSE students Mr Rohit Kumar Jupalle, Mr Dinesh Sai Sandeep Desu, and Mr Nikhil Kethavath to develop and patent an innovative invention.,” The team’s invention, titled “A SYSTEM FOR VISION-BASED INTELLIGENT SHELF MANAGEMENT SYSTEM AND A METHOD THEREOF,” has been officially filed and published with Application Number 202441039394 in the Patent Office Journal. This invention showcases the academic excellence and collaborative spirit within the institution, as faculty members and students work together to push the boundaries of technology and create solutions with real-world impact.
This significant achievement not only highlights the creativity and dedication of the individuals involved but also underscores the institution’s commitment to fostering a culture of innovation and research. The publication of this invention paves the way for further exploration and development in the field of intelligent shelf management systems, demonstrating the potential for transformative contributions to the industry.
Abstract
This research offers the best solution to improve the business in the retail realm, maintaining On- Shelf Availability (OSA) is vital for customer satisfaction and profitability. Traditional OSA methods face accuracy challenges, prompting a shift to deep learning models like YOLO and CNN. However, data quality remains a hurdle. This research introduces OSA, a novel semi-supervised approach merging ’semi-supervised learning’ and ’on-shelf availability’ with YOLO. It reduces human effort and computation time, focusing on efficient empty-shelf detection. Implementing a Vision-Based Intelligent Shelf Management System empowers retailers with real-time insights, revolutionizing decision-making. The model is optimized for diverse devices and provides practical solutions for efficient retail operations. Balancing model complexity, size, latency, and accuracy, the research paves the way for an advanced, data-driven shelf management approach, contributing to improved shopping experiences and business profitability
Practical Implementation and the Social Implications Associated
1. The present invention is a time-saving method in maintaining the stocks.
2. The use Vision-Based Intelligent Shelf-Management System provide a well alternative in reducing the labor efforts.
3. The system will help in terms of self-management system using machine learning techniques to optimize restocking decisions.
The present invention can be used in shopping malls and business areas for enhancing customer experiences and business and few application areas are:
• Smart City and smart Village
This technique and system can reduce the human efforts in identifying vacant slots for items in business areas and provides necessary inputs to fill the same within a time frame.
• Automobile Industry
The system can be easily integrated with the showrooms to identify the empty spaces and inform to get it fill with products.
Collaborations
SRM AP Faculties and UROP Students
Future research plan
In the future, different deep learning and machine learning methods can be merged to explore better performance in identifying overlapping objects.