Fast Underwater Optical Beacon Finding and High Accuracy Visual Ranging Method Based on Deep Learning

Zhang, Bo; Zhong, Ping; Fu, Yang; Zhou, Tian; Shen, Lingfei

doi:10.3390/s22207940

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…Training with these synthetic datasets successfully reduces the error in docking position detection. Zhang [47] proposed a rapid monocular camera localization method for small 4-light beacons. This approach employs a YOLOV5 model with a Coordinated Attention (CA) mechanism, achieving a prediction accuracy of 96.1% and a recall rate of 95.1%.…”

Section: Deep Learning Algorithmsmentioning

confidence: 99%

Review of underwater visual navigation and docking: advances and challenges

Li,

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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The widespread utilization of autonomous underwater vehicles in marine science and engineering has underscored the paramount importance of precise underwater navigation and docking capabilities for underwater robots. Underwater vision navigation systems serve as a pivotal technology, encompassing various domains such as computer vision, image processing, and machine learning. Nevertheless, they confront a series of challenges. This review aims to consolidate the most recent research advancements in underwater vision navigation systems, with a particular focus on critical aspects such as beacon design, monocular and binocular vision, and detection algorithms. At present, underwater beacons can be categorized into active and passive types, with the flexibility to choose between them contingent upon the specific task and environment. On the other hand, relative to the prevalent use of monocular vision, binocular vision systems offer the potential to furnish more accurate depth information, and the seamless integration of monocular and binocular vision systems enhances the precision and stability of navigation tasks. Concerning detection algorithms, traditional feature extraction methods and deep learning approaches each exhibit their merits and drawbacks, but deep learning methods demonstrate immense potential in complex underwater environments. By summarizing and analyzing the above research results, the application of each key technology of underwater vision navigation and docking is sorted out, and its further development direction is foreseen.

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Section: Deep Learning Algorithmsmentioning

confidence: 99%

Review of underwater visual navigation and docking: advances and challenges

Li,

Sun

2024

Sixth Conference on Frontiers in Optical Imaging and Technology: Imaging Detection and Target Recognition

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Autonomous docking system for UUV based on cooperative guidance of multiple markers: Design and experiment

Qu,

Xie,

et al. 2024

Ocean Engineering

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Water-to-air unmanned aerial vehicle (UAV) based rolling shutter optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN)

Tsai,

Chang,

Chow

2024

Opt. Express

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Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted in oceanic research activities. As the number of underwater sensors and AUVs is growing quickly, the bandwidth requirements are increasing accordingly. In this work, we put forward and demonstrate a large field-of-view (FOV) water-to-air unmanned aerial vehicle (UAV) based optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN) for the first time to the best of our knowledge. As the UAVs are embedded with complementary-metal-oxide-semiconductor (CMOS) cameras, there is no need to install OCC receivers (Rxs), reducing the deployment cost. Moreover, the large photo-sensitive area of the CMOS camera can support a large FOV OCC transmission without the need for precise optical alignment. Here, by utilizing the column matrix identification during the rolling shutter pattern decoding in the CMOS image sensor, the scintillation caused by water turbulence can be reduced. Besides, in the outdoor and windy environment, the UAV will experience significant movement caused by the wind making it very difficult to capture stable OCC frames in the CMOS image sensor. Here, we propose and demonstrate utilizing GRU-NN, which is a special realization of the recurrent neural network (RNN) with memory cells capable of learning the time-domain dependent signals. It is shown that the GRU-NN can learn effectively from successive image frames in time-domain and produce correct prediction even under the windy and unstable UAV flying environment. Experimental results reveal that the proposed GRU-NN can outperform the previous pixel-per-symbol labeling neural network (PPS-NN), and also can significantly reduce the computation time when compared with long-short-term-memory-neural-network (LSTM-NN). The proposed system can decode 4-level pulse-amplitude-modulation (PAM4) rolling shutter OCC patterns at data rates of 5.4 kbit/s and 3.0 kbit/s under clear and cloudy water, respectively, fulfilling the pre-forward error correction bit-error rate (pre-FEC BER = 3.8 × 10−3). We also demonstrate that the UAV based OCC system can support data rates of 5.4 kbit/s, 4.2 kbit/s, and 3.0 kbit/s at distances of 2.2 m, 3.2 m, and 4.2 m, respectively, at outdoor and windy environments.

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Fast Underwater Optical Beacon Finding and High Accuracy Visual Ranging Method Based on Deep Learning

Cited by 5 publications

References 31 publications

Review of underwater visual navigation and docking: advances and challenges

Review of underwater visual navigation and docking: advances and challenges

Autonomous docking system for UUV based on cooperative guidance of multiple markers: Design and experiment

Water-to-air unmanned aerial vehicle (UAV) based rolling shutter optical camera communication (OCC) system with gated recurrent unit neural network (GRU-NN)

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