AN IMAGE-BASED INVENTORY MANAGEMENT SYSTEM FOR REAL-TIME STOCK TRACKING USING DEEP LEARNING
DOI:
https://doi.org/10.33003/fjs-2026-1003-4696Keywords:
Inventory Management, Image Recognition, Deep Learning, Convolutional Neural Networks, Automated.Abstract
Image recognition and deep learning inventory management system (IMS) to track stock in real-time. Contrary to the conventional approach that depends on a manual entry, barcoding, or RFID to identify and count products, this system uses convolutional neural networks (CNNs) to identify and count products using the images captured using the webcam. The system is powered by an interface being developed on React and Tailwind CSS which provides real-time dashboards, analytics, and automated updates on stock, minimizing human error and increasing the efficiency of the operations. The outcomes of the experiments have shown high accuracy when working under controlled conditions, and this can be potentially scaled to retail, logistics, and manufacturing. Problems like different lighting and occlusion are solved, and it is suggested to changes in the future to make it robust. This work provides a demonstration of concept of vision based inventory management even filling the gap between theoretical computer vision progress and the applications in the supply chain.
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Copyright (c) 2026 Emmanuel Ayodele; Victor Oluwaseyi Sodeinde, Oluwafunke Adebisi Bada
This work is licensed under a Creative Commons Attribution 4.0 International License.
