A Sequence-Aware Framework for Maritime Signal Flag Recognition and Semantic Interpretation
Year: 2026
Type: article
Abstract: Automatic recognition of maritime signal flags requires not only identifying individual flags but also reconstructing their ordered combinations and interpreting the corresponding maritime meanings. This study develops a sequence-aware framework that integrates visual perception, ordered sequence reconstruction, reliability validation, and International Code of Signals (ICS)-based semantic interpretation. A maritime signal flag dataset containing 7865 images from 40 ICS categories was collected using a single small model vessel in sunny weather. The visual perception module employs Flag-YOLO, an enhanced YOLO11-based detector integrating DualConv, GSConv, and Dynamic Head to improve the recognition of small, distant, blurred, and wind-deformed flags. Detected flags are subsequently arranged in top-to-bottom order, validated using confidence and spatial-consistency constraints, and interpreted using a 313-entry ICS subset restricted to single-group hoists of up to three flags without substitute pennants. Flag-YOLO achieves 93.1% [email protected] and 61.9% [email protected]:0.95 with 2.1 M parameters. In a sequence-level evaluation of 1500 samples, the proposed approach achieves 92.9% accuracy, compared with 88.5% for YOLO11n, with the largest improvement of 7.2 percentage points observed for three-flag sequences. The results demonstrate the effectiveness of integrating visual perception, structured sequence recognition, and domain knowledge for automated maritime visual communication understanding, providing a potential perception component for intelligent maritime systems.
DOI: 10.3390/jmse14212099
Authors: Shengxing Wang, Yong Yin, Qianfeng Jing
Institution: Dalian Maritime University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225419415