Title of the paper: "Detection of ships in aerial images using the RTMDET-ROTATED method"
Student: Tran Nguyen Chi Huy - KTPM2021.KTPM20211 - Main author
Supervisors:
M.Sc Vo Duy Nguyen
M.Sc Dinh Nguyen Anh Dung
Paper Summary:
Currently, thanks to the development of unmanned aerial vehicles, detecting objects in images captured from above has become a prominent topic in the field of computer vision. However, traditional object detection methods still cannot indicate the orientation of objects and the bounding boxes overlap if the image has a dense object density. Therefore, the problem of rotated object detection has emerged. Among them, detecting ships is one of the issues that attract much attention due to its high application in reality, especially when the sovereignty of national waters and maritime safety face many challenges and changes. To overcome these difficulties and meet the demand, many studies have focused on developing effective methods. In this study, we experimentally evaluate the real-time object detection model, called RTMDet (Real-Time Models for Object Detection), with two backbones CSPNeXt and CSPDarkNet on the DOTA-ship dataset. This dataset was created to meet the specific needs of ship detection-related applications. DOTA-ship also reproduces most of the challenges in real-life situations. Finally, we analyze the results and provide detailed comments, analyzing RTMDet when trained on the DOTA-ship dataset, thereby suggesting directions for future research to improve methods in the future.
"I sincerely express my gratitude to the Faculty of Software Engineering, Multimedia Communication Department, and the UIT-Together research group for creating conditions that have helped me research and complete this paper."
The 26th National Conference on Electronics, Communications, and Information Technology, REV-ECIT 2023, was held on December 16, 2023.\
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Ha Bang - Media Collaborator, University of Information Technology
Nhat Hien - Translation Collaborator, University of Information Technology