Automatic detection of ship draft marks and draft reading are critical tasks in intelligent port management and maritime monitoring. This paper presents a lightweight ship draft detection method based on DeepLabv3+, incorporating the SeaFormer backbone network, DenseASPP module, and PointRend boundary refinement technique. The proposed method achieves efficient waterline segmentation and accurate draft depth estimation. The SeaFormer backbone significantly enhances feature extraction capability while reducing computational complexity through its squeeze-enhanced axial attention mechanism. The DenseASPP module strengthens the perception of waterline regions by capturing multi-scale contextual information, and the PointRend module further refines boundary details. Experimental results demonstrate that the proposed method outperforms mainstream segmentation models in terms of segmentation accuracy (mIoU), parameter efficiency, and real-time performance, validating its suitability for resource-constrained scenarios. This approach provides an efficient and reliable solution for ship draft detection and smart shipping applications.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Lightweight ship draft reading framework based on DeepLabv3+ and SeaFormer


    Contributors:
    Luo, Shaohua (editor) / Saxena, Akash (editor) / Wang, Ranxu (author) / Ran, Xin (author) / Huang, Jiani (author)

    Conference:

    Fourth International Conference on Electronics Technology and Artificial Intelligence (ETAI 2025) ; 2025 ; China, China


    Published in:

    Proc. SPIE ; 13692 ; 1369259


    Publication date :

    2025-07-24





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

    Sharma, Rohit / Dahatonde, Shubham / Acharjya, Srimanta Kumar et al. | IEEE | 2024


    Very Large Portable Remote Ship Draft Reading Device

    BROWN CHRISTOPHER | European Patent Office | 2018

    Free access

    Ship draft measuring device

    HUA WENBO | European Patent Office | 2021

    Free access

    Unmanned Driving System Based on DeepLabV3+ Semantic Segmentation

    Wang, Hongyu / Ma, Jiefei / Chi, Haifei | IEEE | 2021


    Ship draft measurement method

    YOU XIAOQIN | European Patent Office | 2021

    Free access