Warehouse Drone Inventory Management System Using OpenCV

This research investigates the application of drone technology for enhancing inventory management efficiency. Utilizing DJI Tello Drones with integrated cameras, controlled via a Wi-Fi connection, and supported by OpenCV software on a PC, this study evaluates the effectiveness of drones in capturing and processing warehouse inventory data. The captured video feeds are analyzed for detection and recognition, with the results integrated into a real-time database. The study uses three ranges of 20 m, 50 m, and 100 m for the distance between the drone and the receiver device to analyze the distance factor affecting the latency. It is found that lower range between the drone and the receiver device results in better latency level. The experiment is conducted using the 20m distance for reduced latency. Experimental results demonstrate that drones can significantly reduce the time required for inventory tasks, with a recorded time of 2 minutes to document 16 items, compared to 15 minutes for manual checks. Additionally, the drones maintained a high detection accuracy, with an average successful detection distance of 0.55 meters for 8x8 cm QR codes. These findings substantiate the potential of drones and computer vision technology to optimize warehouse operations and supply chain management by increasing inventory accuracy, reducing labor costs, and improving responsiveness. This research contributes to the advancement of drone-enabled solutions across various industries, offering a pathway towards more efficient and agile inventory management practices.