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@article{197435,
        author = {Mr. Khaja Pasha Shaik and Mr. Ayman Khan and Mr. Atif Riyan Ahmed and Mr. Fawaz Naseeruddin},
        title = {Marine Safety Enhancement using Faster R-CNN & VGG16 for Ship Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {6727-6735},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197435},
        abstract = {Maritime surveillance plays a crucial role in ensuring marine safety and preventing illegal activities such as piracy, smuggling, and illegal fishing. Traditional ship detection systems rely on classical image processing techniques and radar- based algorithms, which often suffer from high false alarm rates and limited adaptability to complex marine environments. This research proposes a deep learning-based ship detection system using the Faster Region-Based Convolutional Neural Network (Faster R-CNN) framework with VGG16 as the backbone architecture. The proposed model analyses range- compressed airborne radar data to detect ships efficiently. Experimental results demonstrate that the proposed method significantly improves detection accuracy and reduces false alarms compared to traditional techniques such as CFAR and SVM-based detection methods. The system also supports real-time maritime monitoring applications.},
        keywords = {},
        month = {April},
        }