Optimal Identification for Objects in Problems on Recognition by Unmanned Underwater Vehicles

Nyrkov, Anatoliy; Sokolov, Sergei; Alimov, Oleg M.; Chernyi, Sergei; Доровской, Владимир Алексеевич

doi:10.3103/s0146411620080234

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Unmanned underwater vehicles (UUV) have recently gained great importance due to their role in carrying out tasks that are sometimes difficult for humans to perform on their own. These systems have important scientific applications [1][2][3][4], and military applications [5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Low-cost noise maker for testing acoustically guided unmanned vehicles

Mousa,

Tork,

Abdallah

2023

J. Phys.: Conf. Ser.

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Testing the performance of the Acoustically Guided Unmanned Underwater Vehicle (AGUUV) prior to its deployment is essential to ensure its ability to perform the required tasks accurately. These systems can be deployed from boats or helicopters to scan areas of military and economic importance for threats. These threats such as aircrafts carriers, ships, submarines, divers, and autonomous surface vehicles. The problem of implementing a low-cost high-performance noise maker (LCNM) which is suitable for being used as a test target instead of different threats for AGUUVs is the motivation of this paper. Lab experiments and sea trials are performed using the proposed noise maker to demonstrate the proper operation of the AGUUV. Lab experiments showed that the radiated noise of the proposed noise maker has high power in the frequency band between 5 to 8 kHz. The measured signal to noise ratio (SNR) for the introduced LCNM is 30 dB in this band. The experimental results presented in this paper show the feasibility of using the proposed LCNM effectively to test the AGUUVs.

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Section: Introductionmentioning

confidence: 99%

Low-cost noise maker for testing acoustically guided unmanned vehicles

Mousa,

Tork,

Abdallah

2023

J. Phys.: Conf. Ser.

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“…As the functional portal of the underwater operation system, the subsea X-tree is the key equipment for offshore oil and gas production and downhole operation [3]. Studies on fatigue relief [4] and resistance [5] of subsea X-tree drilling continue to be updated, but most of the scientific experimental focus is on strength and mechanical properties, while the intelligent applications in this field are relatively absent. Therefore, the identification of subsea X-tree key components is the key step for intelligent research in this field.…”

Section: Introductionmentioning

confidence: 99%

“…This efficient training strategy effectively makes up for the problem of the small number of underwater datasets and the single scene, which effectively improves the recognition accuracy. (5) This paper establishes an underwater oil extraction tree part identification dataset under multiple backgrounds. It is worth mentioning that the subsea X-tree cannot obtain effective image and video information directly, so we build some models of subsea X-tree parts by using 3D printing technology and build part datasets under different backgrounds, including underwater environment backgrounds.…”

Section: Introductionmentioning

confidence: 99%

An Improved Underwater Recognition Algorithm for Subsea X-Tree Key Components Based on Deep Transfer Learning

Zhao

Han

et al. 2022

JMSE

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It is promising to detect or maintain subsea X-trees using a remote operated vehicle (ROV). In this article, an efficient recognition model for the subsea X-tree component is proposed to assist in the autonomous operation of unmanned underwater maintenance vehicles: an efficient network module, SX(subsea X-tree)-DCANet, is designed to replace the CSPBlock of YOLOv4-tiny with ResBlock-D and combine with the ECANet attention module. In addition, two-stage transform learning is used for the insufficiency of underwater target recognition samples as well as the overfitting caused by the subsea target recognition model, thereby providing an effective learning strategy for traditional subsea target recognition. A mosaic data augment algorithm and cosine annealing algorithm are also utilized for better accuracy of network training. The results of ablation studies show that the mean Average Precision (mAP) and speed of the improved algorithm are increased by 1.58% and 10.62%, respectively. Multiple field experiments on the laboratory, experimental pool, and the hydro-electric station prove that the recognition algorithm and training strategy present in this article can be well applied in subsea X-tree component recognition, and can effectively promote the development of intelligent subsea oil extraction projects.

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