Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection

Khan, Sulaiman; Ullah, Inam; Ali, Farhad; Shafiq, Muhammad; Ghadi, Yazeed Yasin; Kim, Taejoon

doi:10.3389/fmars.2022.1094915

Cited by 12 publications

(3 citation statements)

References 33 publications

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“…Underwater photography is one of important environmental perception tools which can be used in object detection [36], image processing [37], visual navigation [38], and so on. With realistic 3D display and support for multi-view cameras in UE4, underwater camera simulation is feasible and has high-fidelity imagery [15].…”

Section: • Gps Sensormentioning

confidence: 99%

Design and Implementation of Autonomous Underwater Vehicle Simulation System Based on MOOS and Unreal Engine

Zhang¹,

Yang²,

Liu³

et al. 2023

Electronics

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An effective autonomous underwater vehicle (AUV) simulation system can greatly improve development efficiency and reduce the cost and risk of actual equipment operation. In this paper, a comprehensive simulation system is developed using Mission Oriented Operating Suite (MOOS) and Unreal Engine 4 (UE4). The former provides an open-source development framework and application components, which are widely used in the field of underwater robots. The latter is a well-known game development engine that has realistic effects and various plugins. As far as we know, there are few simulation systems based on MOOS and UE4. Moreover, a modular architecture for the simulation system is designed and implemented in the paper. The developed system consists of four subsystems, including the simulation editing and controlling subsystem, shore-based operation subsystem, simulation engine subsystem, and three-dimensional (3D) visualization subsystem. These subsystems have modular characteristics and independent functions and communicate with each other through network sockets. Through the system, users can customize simulation scenarios, send missions and control simulation progress from the graphic user interface. And the system can be used in operation training, software testing, algorithm verification of AUV, etc.

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Section: • Gps Sensormentioning

confidence: 99%

Design and Implementation of Autonomous Underwater Vehicle Simulation System Based on MOOS and Unreal Engine

Zhang¹,

Yang²,

Liu³

et al. 2023

Electronics

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“…The former focuses on improving the quality of degraded underwater images caused by light dispersion and color distortion, while the latter aims to locate and identify underwater targets more accurately and efficiently. Various image enhancement techniques have been investigated in the literature for improving contrast, correcting color shifts, and sharpening edges in underwater images [7][8][9]. Furthermore, other studies aim to counteract the negative effects of image blur by enhancing the network architecture [10][11][12] and refining training strategies [13].…”

Section: Underwater Object Detectionmentioning

confidence: 99%

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

Zhao,

Yun,

Yuan

et al. 2023

JMSE

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Underwater target detection plays a crucial role in marine environmental monitoring and early warning systems. It involves utilizing optical images acquired from underwater imaging devices to locate and identify aquatic organisms in challenging environments. However, the color deviation and low illumination in these images, caused by harsh working conditions, pose significant challenges to an effective target detection. Moreover, the detection of numerous small or tiny aquatic targets becomes even more demanding, considering the limited storage and computing power of detection devices. To address these problems, we propose the YOLOv7-CHS model for underwater target detection, which introduces several innovative approaches. Firstly, we replace efficient layer aggregation networks (ELAN) with the high-order spatial interaction (HOSI) module as the backbone of the model. This change reduces the model size while preserving accuracy. Secondly, we integrate the contextual transformer (CT) module into the head of the model, which combines static and dynamic contextual representations to effectively improve the model’s ability to detect small targets. Lastly, we incorporate the simple parameter-free attention (SPFA) module at the head of the detection network, implementing a combined channel-domain and spatial-domain attention mechanism. This integration significantly improves the representation capabilities of the network. To validate the implications of our model, we conduct a series of experiments. The results demonstrate that our proposed model achieves higher mean average precision (mAP) values on the Starfish and DUO datasets compared to the original YOLOv7, with improvements of 4.5% and 4.2%, respectively. Additionally, our model achieves a real-time detection speed of 32 frames per second (FPS). Furthermore, the floating point operations (FLOPs) of our model are 62.9 G smaller than those of YOLOv7, facilitating the deployment of the model. Its innovative design and experimental results highlight its effectiveness in addressing the challenges associated with underwater object detection.

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“…Marine vehicles use these sensors for communication in underwater environments, and this technology is referred to as Marine Underwater Sensor Networks (MUSNs) [39][40][41][42][43][44][45][46][47]. These networks are established by marine forces and other parties for whom the acquisition of information and knowledge regarding the sea and life in the sea is of interest [48][49][50][51][52][53]. These sensor nodes have a limited communication range, and are equipped with batteries with limited charge, thus limiting computation.…”

Section: Introductionmentioning

confidence: 99%

CR-NBEER: Cooperative-Relay Neighboring-Based Energy Efficient Routing Protocol for Marine Underwater Sensor Networks

Hussain

Ullah

et al. 2023

JMSE

Self Cite

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This paper proposes a Cooperative-Relay Neighboring-Based Energy-Efficient Routing (CR-NBEER) protocol with advanced relay optimization for MUSN. The utilization of the relay nodes, among all other sensor nodes, makes it possible to achieve node-to-node deployment. The proposed method focuses only on cooperation and relay optimization schemes. Both schemes have previously been implemented, and thus the proposed method represents the extended version of the Neighboring-Based Energy-Efficient Routing (NBEER) protocol. Path loss, end-to-end delay, packet delivery ratio, and energy consumption parameters were considered as part of the performance evaluation. The average performance was revealed based on simulations, where the overall average EED of Co-UWSN was measured to be 35.5 ms, CEER was measured to be 26.7 ms, NBEER was measured to be 27.6 ms, and CR-NBEER was measured to be 19.3 ms. Similarly, the overall EC of Co-UWSN was measured to be 10.759 j, CEER was measured to be 8.694 j, NBEER was measured to be 8.309 j, and CR-NBEER was measured to be 7.644 j. The overall average PDR of Co-UWSN was calculated to be 79.227%, CEER was calculated to be 66.73.464%, NBEER was calculated to be 85.82%, and CR-NBEER was calculated to be 94.831%. The overall average PL of Co-UWSN was calculated at 137.5 dB, CEER was calculated at 230 dB, NBEER was calculated at 173.8 dB, and CR-NBEER was calculated at 79.9 dB. Based on the simulations and evaluations, it was observed that the cooperation and relay optimization scheme outperformed previous schemes.

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Deep learning-based marine big data fusion for ocean environment monitoring: Towards shape optimization and salient objects detection

Cited by 12 publications

References 33 publications

Design and Implementation of Autonomous Underwater Vehicle Simulation System Based on MOOS and Unreal Engine

Design and Implementation of Autonomous Underwater Vehicle Simulation System Based on MOOS and Unreal Engine

YOLOv7-CHS: An Emerging Model for Underwater Object Detection

CR-NBEER: Cooperative-Relay Neighboring-Based Energy Efficient Routing Protocol for Marine Underwater Sensor Networks

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