A Practical Deep Learning-Based Inventory Management system for object-detection of inventory items and stock count
In the fast-paced business world of today, efficient inventory management is a critical factor in ensuring the smooth operation of supply chains and, ultimately, in enhancing customer satisfaction. This project introduces an innovative solution in the form of a mobile device-centric web application designed to streamline inventory management. It offers real-time visibility into inventory levels, utilizing a deep learning model for the automated detection and updating of item quantities. The interface allows inventory managers to easily access detailed item information and monitor inventory status. Simultaneously, a deep learning model is deployed to identify items within live video streams captured by devices, reducing the potential for errors and eliminating the need for manual intervention. A standout advantage of this system is its real-time inventory updating capability. When the deep learning model identifies items, it communicates directly with the central inventory database, ensuring swift updates to inventory quantities. The constantly updated inventory list is readily accessible on the website's frontend, empowering users to make informed decisions regarding inventory management.