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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Lightweight ship draft reading framework based on DeepLabv3+ and SeaFormer


    Beteiligte:
    Luo, Shaohua (Herausgeber:in) / Saxena, Akash (Herausgeber:in) / Wang, Ranxu (Autor:in) / Ran, Xin (Autor:in) / Huang, Jiani (Autor:in)

    Kongress:

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


    Erschienen in:

    Proc. SPIE ; 13692 ; 1369259


    Erscheinungsdatum :

    24.07.2025





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    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 | Europäisches Patentamt | 2018

    Freier Zugriff

    Ship draft measuring device

    HUA WENBO | Europäisches Patentamt | 2021

    Freier Zugriff

    Unmanned Driving System Based on DeepLabV3+ Semantic Segmentation

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


    Ship draft measurement method

    YOU XIAOQIN | Europäisches Patentamt | 2021

    Freier Zugriff