Ship detection in maritime environments presents significant challenges due to factors like dramatic scale variations, complex backgrounds, and flexible viewpoints in drone-captured images, traditional object detection methods struggle with these conditions, requiring advanced deep learning techniques for effective identification and in this paper, we propose a deep learning-based ship detection system designed to overcome these challenges and the system integrates Gray-Level Co-occurrence Matrix (GLCM) and Histogram of Oriented Gradients (HOG) for feature extraction, enhancing the model's ability to differentiate ships from cluttered backgrounds, a deep neural network with Dropout layers is employed to improve generalization and reduce overfitting. The model Is trained on synthetic datasets and evaluated using key performance metrics like precision, recall, F1-score, accuracy, and ROC-AUC curves, results demonstrate high detection accuracy, effectively minimizing false positives and false negatives, additionally, threshold optimization enhances detection performance across diverse maritime conditions, to further improve efficiency, future research may incorporate Vision Transformers (ViTs) to enhance contextual understanding and reinforcement learning for adaptive detection in dynamic environments and the proposed system contributes to real-time ship monitoring and maritime security, offering a scalable solution for autonomous surveillance and vessel tracking.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Toward Real-Time Maritime Surveillance: Deep Learning and Feature Fusion for Ship Detection


    Beteiligte:


    Erscheinungsdatum :

    28.05.2025


    Format / Umfang :

    354113 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Online learning for ship detection in maritime surveillance

    Wijnhoven, Rob / Rens, Kris van / Jaspers, Egbert G.T. et al. | Tema Archiv | 2010


    Data Fusion Architecture for Maritime Surveillance

    Gad, A. / Farooq, M. / International Society of Information Fusion et al. | British Library Conference Proceedings | 2002


    Research on Real-Time Ship Detection Using Deep Learning

    Yu, Jingming / Wang, Jie / Ren, Rong et al. | IEEE | 2022


    Data fusion architecture for Maritime Surveillance

    Gad, A. / Farooq, M. | IEEE | 2002


    An Automated SAR-based Method for Ship Detection in Maritime Surveillance System

    Xu, Zidong / Zhang, Di / Fan, Liang et al. | IEEE | 2023