Abstract

Effective inventory management is key to retail and warehousing operations to guarantee product readiness and avoid losses from stockouts or excess inventory. StockVision is a computer vision and deep learning-powered AI inventory tracking system that automates shelf monitoring. The system utilizes YOLOv5, OpenCV, and a common laptop camera to identify and count items. In contrast to traditional inventory management methods that are dependent on billing information or manual inventory counting, StockVision offers an autonomous, automated means of tracking product availability. The system records real-time video of store shelves, feeds it into a trained object detection model, and raises an alarm when the inventory goes below a pre-determined threshold. This eliminates the delay in replenishment, cuts down labor expense of manual counts, and optimizes operations. StockVision has widespread use in retail inventory management, grocery store automation, warehouse tracking, and smart inventory systems. Possible future developments include IoT integration for real-time tracking, AI-powered demand forecasting, and cloud-based analytics for better decision-making. This work contributes to the development of automated inventory monitoring, providing an efficient, scalable, and adaptive solution for contemporary retail and logistics businesses.

Keywords

Deep Learning, YOLOv5, OpenCV, Automation, Real-time Monitoring, AI-based System, IoT Integration.

Taj Iram Khan

Affiliation

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ML-Based Shelf Tracking And Low-Stock Warning System