Research on the identification and distribution of biofouling using underwater cleaning robot based on deep learning

Zhao, Wangyuan; Han, Fenglei; Qiu, Xinjie; Zhao, Yuemin; Zhang, Jiawei

doi:10.1016/j.oceaneng.2023.113909

Cited by 18 publications

(1 citation statement)

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“…FIDCE enhances image quality and accurately identifies biofouling, which is vital for ship maintenance. MFONet outperforms classical algorithms, offering superior speed and accuracy, enabling automated cleaning and maintenance planning for underwater vehicles [81]. Laura A. Martinho et al propose a learning-based approach for enhancing the quality of underwater images.…”

Section: Advantage Of Deep Learning Relative To the Conventional Methodsmentioning

confidence: 99%

Advancements in Underwater Navigation: Integrating Deep Learning and Sensor Technologies for Unmanned Underwater Vehicles

Merveille,

Jia,

2024

Preprint

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Unmanned Underwater Vehicles (UUVs) are pivotal in ocean exploration, research, and various industrial activities such as marine mining and offshore engineering. However, traditional methods of navigating these vehicles face significant challenges, mainly due to the inefficacy of electromagnetic waves in water, leading to signal loss. To address these limitations, researchers are increasingly turned learning, an artificial intelligence technique capable of learning from data-enhancing underwater navigation, mainly through visual Simultaneous Localization and Mapping (SLAM). This paper explores integrating deep learning methodologies and sensor technologies to revolutionize underwater navigation for UUVs. Proprioceptive sensors, along with exteroceptive sensors, are crucial in accurately measuring and comprehending the underwater environment. Additionally, the paper provides detailed insights into the processes of underwater SLAM, camera-based underwater positioning systems, sonar systems for underwater navigation, and the utilization of Lidar in underwater navigation. Furthermore, it delves into applying deep learning techniques in underwater SLAM, offering a comprehensive understanding of the innovative processes driving advancements in underwater vehicle navigation. By leveraging these advancements, this research aims to improve underwater navigation systems' precision, reliability, and adaptability, thereby unlocking new frontiers in ocean exploration and industrial applications for UUVs.

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Section: Advantage Of Deep Learning Relative To the Conventional Methodsmentioning

confidence: 99%