Research Article
Enhancing Maritime Safety with Deep Learning for Ship Identification
@INPROCEEDINGS{10.1007/978-3-031-81168-5_20, author={K. Sripal and Kotra Akshay and Avula Shiva Sai and Rebanamoni Sravan Kumar Reddy}, title={Enhancing Maritime Safety with Deep Learning for Ship Identification}, proceedings={Broadband Communications, Networks, and Systems. 14th EAI International Conference, BROADNETS 2024, Hyderabad, India, February 16--17, 2024, Proceedings, Part I}, proceedings_a={BROADNETS}, year={2025}, month={2}, keywords={Ship Detection Surveillance Systems Maritime Safety Vessel Classification CNN}, doi={10.1007/978-3-031-81168-5_20} }- K. Sripal
Kotra Akshay
Avula Shiva Sai
Rebanamoni Sravan Kumar Reddy
Year: 2025
Enhancing Maritime Safety with Deep Learning for Ship Identification
BROADNETS
Springer
DOI: 10.1007/978-3-031-81168-5_20
Abstract
To Enhance maritime safety using advanced deep learning techniques, particularly CNNs, to accurately identify and classify ships in satellite and surveillance images. The maritime industry’s critical role in global trade demands more sophisticated technologies to ensure safety, security, and efficient navigation. The ship identification system’s applications are diverse, including classifying cargo vessels, tankers, fishing boats, and distinguishing legitimate ships from potential threats. Real time monitoring capabilities facilitate proactive responses to emergencies and security risks. The expected findings and results of the project indicate successful object identification in the environment contributes to a safer and more secure maritime environment by leveraging deep learning and CNNs for ship classification and identification. By enabling real-time monitoring and integration into surveillance systems, the system enhances maritime safety, facilitating efficient and secure global trade and navigation.
