AI-Powered Container Yard Management System using Object Detection and OCR

Abstract

Efficient management of container yards is a critical challenge in modern logistics and port operations, where manual identification and tracking of containers often lead to delays, errors, and increased operational costs. This paper presents an AI-Powered Container Yard Management System that automates the detection and identification of containers using advanced com- puter vision techniques. The proposed system integrates Object Detection and Optical Character Recognition to accurately detect containers from images or real-time video streams and extract their identification numbers. The methodology involves image preprocessing, followed by container detection using a deep learning model such as YOLO (You Only Look Once), which provides real-time performance with high accuracy. The detected regions are then processed using OCR techniques to recognize and extract alphanumeric container IDs. The extracted information is stored in a database for efficient tracking and management. Experimental results demonstrate that the system achieves high detection accuracy and reliable text extraction under stan- dard conditions, significantly reducing manual effort and im- proving operational efficiency. Although performance may vary under challenging conditions such as low lighting or occlusion, the proposed system provides a scalable and effective solution for automated container yard management. This approach has strong potential for real-world deployment in logistics, shipping, and port management systems.Efficient management of container yards is a critical challenge in modern logistics and port oper- ations, where manual identification and tracking of containers often lead to delays, errors, and increased operational costs. This paper presents an AI-Powered Container Yard Management Sys- tem that automates the detection and identification of containers using advanced computer vision techniques. The proposed system integrates Object Detection and Optical Character Recognition to accurately detect containers from images or real-time video streams and extract their identification numbers. The methodology involves image preprocessing, followed by container detection using a deep learning model such as YOLO (You Only Look Once), which provides real-time performance with high accuracy. The detected regions are then processed using OCR techniques to recognize and extract alphanumeric container IDs. The extracted information is stored in a database for efficient tracking and management. Experimental results demonstrate that the system achieves high detection accuracy and reliable text extraction under stan- dard conditions, significantly reducing manual effort and im- proving operational efficiency. Although performance may vary under challenging conditions such as low lighting or occlusion, the proposed system provides a scalable and effective solution for automated container yard management. This approach has strong potential for real-world deployment in logistics, shipping, and port management systems.

Introduction

The paper proposes an AI-based automated system for container yard management to address inefficiencies in traditional manual or semi-automated container tracking, which is often slow, error-prone, and unsuitable for large-scale operations in global trade logistics.

The system uses deep learning-based computer vision techniques, primarily YOLO for real-time object detection, to locate containers in images or video streams. After detection, Optical Character Recognition (OCR) is applied to extract container identification numbers, enabling end-to-end automated container recognition and tracking. The pipeline includes preprocessing steps (noise removal, resizing, and contrast enhancement), object detection, ROI extraction, OCR processing, and database storage of results.

The study also outlines key objectives such as improving detection accuracy, ensuring real-time performance, reducing manual effort, increasing robustness under varying environmental conditions, and building a scalable, cost-effective solution for real-world deployment. It further reviews related work showing the evolution from traditional detection methods to modern deep learning approaches like YOLO, and OCR tools such as Tesseract and EasyOCR, while noting challenges in complex environments.

Conclusion

This paper presents an AI-Powered Container Yard Management System that automates container detection and iden- tification using advanced computer vision techniques. By integrating Object Detection and Optical Character Recognition, the system efficiently detects containers and extracts their identification numbers from images and video streams. The use of deep learning models such as YOLO (You Only Look Once) enables accurate and near real-time detection, while preprocessing techniques improve OCR performance. Experimental results demonstrate that the system achieves high accuracy and reliability under standard conditions, significantly reducing manual effort and improving operational efficiency. However, certain limitations such as sensitivity to lowlight conditions, occlusion, and image quality variations affect overall performance. Despite these challenges, the proposed system provides a scalable and effective solution for automated container yard management. In conclusion, the integration of detection and OCR techniques offers a practical and intelligent approach for modern logistics systems, with strong potential for real-world deploy- ment and further enhancement.

References

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Copyright

Copyright © 2026 Mrs. S. S. Bhosale, Sujal Vijaychandra Patil, Sahil Sanjay Vibhute, Jayesh Ganesh Bagal, Sandip Tanaji Mane. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.