An Inland Port Monitoring System using Aerial and Ground Imagery (GRC 2025 Proceedings)
This study presents a framework for inland ports that uses unmanned aerial vehicle (UAV)-based imaging, and reach stacker sensor data including a camera to monitor port operations. A three-stage identification pipeline detects transportation units (TUs) as the first stage, then markings of detected TUs such as ISO6436 compliant ID codes are detected as the second stage. Finally, a text recognition model extracts their IDs.
Drone-Based Identification of Containers and Semi-Trailers in Inland Ports (LOGMS 2024)
This paper introduces a novel application utilizing drones and deep learning to identify containers and semi-trailers, enhancing inland port operations. With this drone-based image and text recognition system, the basic condition of the yard/storage area can be determined at any time without using (human) labor, eliminating the need for manual inspections. We use a two-step recognition process, first localizing the text ID and then reading/identifying it.
Poster summary of the project
An overview of the goals, staff, and tasks of the project is provided by the poster for the "Digital Total" event at the University of Hamburg.
