Products recognition in the racks of visicooler using YOLO | |
| Author | Nguyen Duc Hai |
| Call Number | AIT PJPR PMDS no.24-01 |
| Subject(s) | Optical character recognition Computer vision Image processing |
| Note | A project report submitted in partial fulfillment of the requirements for the degree of Master of Science (Professional) in Data Science and Artificial Intelligence Applications |
| Publisher | Asian Institute of Technology |
| Abstract | Automated product recognition plays a crucial role in the retail sector due to its myriad applications in Computer Vision. Object recognition technology enables retailers to efficiently monitor product placement and presentation on shelves. By analyzing images or video feeds of shelves, retailers can evaluate product arrangement, shelf compliance, and promotional displays. This data empowers retailers to optimize shelf layouts, ensure proper product positioning, and implement effective merchandising strategies to enhance customer engagement and boost profitability. This thesis focuses on utilizing the power of computer vision for product recognition on shelves and identifying empty spaces within visicoolers, leveraging a custom dataset for model training. The objective is to compare different models to determine their accuracy, efficiency, and suitability for product recognition tasks in retail environments. |
| Year | 2024 |
| Type | Project |
| School | School of Engineering and Technology |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Professional Master in Data Science and Artificial Intelligence Applications (PMDS) |
| Chairperson(s) | Chutiporn Anutariya;Cherdsak Kingkan (Co-chairperson) |
| Examination Committee(s) | Vatcharaporn Esichaikul;Chantri Polprasert |
| Scholarship Donor(s) | AIT Scholarships |
| Degree | Project (M. Sc.) - Asian Institute of Technology, 2024 |