Deep Learning-Powered Ship IMO Number Identification on UAV Imagery

• Published in: IEEE access Vol. 12; pp. 107368 - 107384
• Main Author: Cao, Zhi-Bo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Autonomous aerial vehicles; deep learning framework; IMO number identification; Marine vehicles; Navigation; Object detection; Performance evaluation; Recognition; Ship detection; Text detection; Text recognition; UAV-based recognition; Unmanned aerial vehicles
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3438792

The paper presents a novel framework for ship International Maritime Organization (IMO) number Identification using unmanned aerial vehicles (UAVs). It comprises three integrated modules: ship IMO region detection, text detection within detected IMO regions, and IMO number extraction from detected text. Furthermore, considering real-world implementation, the paper details the framework's practical deployment on UAV and proposes an algorithm for efficiently extracting IMO numbers from the detected text. To ensure robust performance evaluation, a comprehensive evaluation metric is established for screening various ship IMO region detection, text detection, and recognition algorithms. Through extensive experimentation, YOLOx_S, DB_R18, and SVTR were identified as optimal for ship IMO region detection, text detection, and text recognition respectively. Finally, we acknowledge the presence of potential false detections in the results and emphasize that while the comprehensive evaluation metric offers valuable insights, it should not be the sole criterion for algorithm selection.