StockSense: A Real-Time, AI-Powered Inventory Management System for SMEs using Pretrained YOLOv8n and Streamlit

Carranza Romero, Dana (2025) StockSense: A Real-Time, AI-Powered Inventory Management System for SMEs using Pretrained YOLOv8n and Streamlit. Masters thesis, Dublin, National College of Ireland.

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Abstract

Deployment of AI computer vision tool to detect inventory in limited technical and budget resources small businesses or SMEs. Implementing YOLO8n as the selected object detection model due to its lightweight and compatibility. As well as Camo Cam for real time detection with a smartphone. The project detects live inventory data each 5 seconds to provide information of the stock available and provide insights of the inventory. Later Streamlit provides a user-friendly interface for analyzing product counting, dashboards of stock composition, and provides recommendations generated by an LLM. Also, a custom trained YOLOv8n was tested but a pretrained model was the best option in the case for accessibility and low-cost solution for SMEs.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Jameel Syed, Muslim UNSPECIFIED
Subjects:	Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence > Computer vision Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence > Computer vision H Social Sciences > HD Industries. Land use. Labor > Small Business Sector
Divisions:	School of Computing > Master of Science in Artificial Intelligence for Business
Depositing User:	Ciara O'Brien
Date Deposited:	24 Jun 2026 11:12
Last Modified:	24 Jun 2026 11:12
URI:	https://norma.ncirl.ie/id/eprint/9398

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