Automatic Mine Detection for Underwater and Surface Vehicles

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Springer

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info:eu-repo/semantics/closedAccess

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The underwater surveillance and support system is currently utilized in the maritime jurisdiction areas of countries to ensure effective solutions for oil and gas drilling and military operations. Comprising a mother ship, an Unmanned Underwater Vehicle, and an Unmanned Surface Vehicle, the system aims to conduct underwater operations in fleet formation while establishing active communication with the ship. Tasks include mine detection, underwater color correction, and underwater-surface communication. Research findings indicate that Naval Forces’ frigates and assault boats protect against harassment attempts on drill ships, while Air Force’s F-16 jets offer aerial protection. Weaknesses in protection systems against underwater threats pose significant challenges for drill and military vessels. To ensure project success, efforts are focused on software development, communication systems installation, data analysis, and collaboration enhancements. Technologies like magnetic sensing, sonar, and camera systems are employed to detect and track underwater mines. Implementing these technologies is crucial for realizing an effective underwater surveillance and support system, ensuring safer navigation for ships and more cost-efficient performance by underwater vehicles. The ability of vehicles to communicate with the ship is critical for coordinated task execution. In this study, a mine detection model was developed for use in AUVs. While the mAP0.5 value of the mine class was 94% with Yolov7, the mAP value was increased to 95% with Yolov8. For the non-mine class, the mAP0.5 value was increased from 47 to 48%.

Açıklama

3rd International Conference on Computing, IoT and Data Analytics, ICCIDA 2024 / Editors:Fausto Pedro García Márquez, Akhtar Jamil, Alaa Ali Hameed, Haixin Wang, Yuxian Zhang, Junyou Yang -- Springer -- ISBN:978-303187153-5 -- 2025.

Anahtar Kelimeler

Maritime security, Mine detection, Underwater surveillance, YOLOv7 and YOLOv8

Kaynak

3rd International Conference on Computing, IoT and Data Analytics, ICCIDA 2024

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387 SIST

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Künye

Bayrak, Ş., Bayraktar, R., Doğan, H.K. (2025). Automatic Mine Detection for Underwater and Surface Vehicles. In: García Márquez, F.P., Jamil, A., Hameed, A.A., Wang, H., Zhang, Y., Yang, J. (eds) Computing, Internet of Things and Data Analytics. ICCIDA 2024. Smart Innovation, Systems and Technologies, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-031-87154-2_58

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