scholarly journals Underwater target recognition methods based on the framework of deep learning: A survey

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142097630
Author(s):  
Bowen Teng ◽  
Hongjian Zhao
Keyword(s):  
Deep Learning ◽  
Autonomous Underwater Vehicle ◽  
Target Recognition ◽  
Development Trend ◽  
Research Field ◽  
Basic Principles ◽  
Technical Problems ◽  
Underwater Image ◽  
Pros And Cons ◽  
Underwater Target

The accuracy of underwater target recognition by autonomous underwater vehicle (AUV) is a powerful guarantee for underwater detection, rescue, and security. Recently, deep learning has made significant improvements in digital image processing for target recognition and classification, which makes the underwater target recognition study becoming a hot research field. This article systematically describes the application of deep learning in underwater image analysis in the past few years and briefly expounds the basic principles of various underwater target recognition methods. Meanwhile, the applicable conditions, pros and cons of various methods are pointed out. The technical problems of AUV underwater dangerous target recognition methods are analyzed, and corresponding solutions are given. At the same time, we prospect the future development trend of AUV underwater target recognition.

scholarly journals Underwater Target Recognition Based on Multi-Decision LOFAR Spectrum Enhancement: A Deep-Learning Approach

2021 ◽  
Vol 13 (10) ◽  
pp. 265
Author(s):  
Jie Chen ◽  
Bing Han ◽  
Xufeng Ma ◽  
Jian Zhang
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Recognition Accuracy ◽  
Target Recognition ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Low Frequency ◽  
Learning Approach ◽  
Decision Algorithm ◽  
Underwater Target

Underwater target recognition is an important supporting technology for the development of marine resources, which is mainly limited by the purity of feature extraction and the universality of recognition schemes. The low-frequency analysis and recording (LOFAR) spectrum is one of the key features of the underwater target, which can be used for feature extraction. However, the complex underwater environment noise and the extremely low signal-to-noise ratio of the target signal lead to breakpoints in the LOFAR spectrum, which seriously hinders the underwater target recognition. To overcome this issue and to further improve the recognition performance, we adopted a deep-learning approach for underwater target recognition, and a novel LOFAR spectrum enhancement (LSE)-based underwater target-recognition scheme was proposed, which consists of preprocessing, offline training, and online testing. In preprocessing, we specifically design a LOFAR spectrum enhancement based on multi-step decision algorithm to recover the breakpoints in LOFAR spectrum. In offline training, the enhanced LOFAR spectrum is adopted as the input of convolutional neural network (CNN) and a LOFAR-based CNN (LOFAR-CNN) for online recognition is developed. Taking advantage of the powerful capability of CNN in feature extraction, the recognition accuracy can be further improved by the proposed LOFAR-CNN. Finally, extensive simulation results demonstrate that the LOFAR-CNN network can achieve a recognition accuracy of 95.22%, which outperforms the state-of-the-art methods.

scholarly journals Visual detection and feature recognition of underwater target using a novel model-based method

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141880899 ◽  
Author(s):  
Daxiong Ji ◽  
Haichao Li ◽  
Chen-Wei Chen ◽  
Wei Song ◽  
Shiqiang Zhu
Keyword(s):  
Feature Recognition ◽  
Visual Detection ◽  
Target Recognition ◽  
Transmission Error ◽  
Underwater Robotics ◽  
Model Based ◽  
Underwater Image ◽  
Important Means ◽  
Underwater Target ◽  
Novel Model

Imaging is an important means to explore the ocean for underwater robotics. The diffuse attenuation coefficient of light in water is one of the most important optical properties of seawater. This article presents a model-based method to analyze the causes of distortion of underwater images. We built a platform for underwater image acquisition and target recognition. The model coefficients were calibrated with images captured underwater and in air. Experiments were carried out to verify the designed algorithm and the transmission error model. The experiments show that the presented method works well in improving the accuracy of feature extraction and recognition of underwater targets.

scholarly journals A Review of Underwater Target Recognition Based on Deep Learning

2021 ◽  
Vol 1881 (4) ◽  
pp. 042031
Author(s):  
Yujie Chen ◽  
Haichun Niu ◽  
Huiwei Chen ◽  
Xiaoling Liu
Keyword(s):  
Deep Learning ◽  
Target Recognition ◽  
Underwater Target

Deep learning architectures for underwater target recognition

2013 ◽  
Author(s):  
Suraj Kamal ◽  
Shameer K. Mohammed ◽  
P. R. Saseendran Pillai ◽  
M. H. Supriya
Keyword(s):  
Deep Learning ◽  
Target Recognition ◽  
Underwater Target ◽  
Learning Architectures

scholarly journals The Sustainable Development of Financial Topic Detection and Trend Prediction by Data Mining

2021 ◽  
Vol 13 (14) ◽  
pp. 7585
Author(s):  
Yunmei Liu ◽  
Shuai Zhang ◽  
Min Chen ◽  
Yenchun Wu ◽  
Zhengxian Chen
Keyword(s):  
Data Mining ◽  
Supply Chain ◽  
Financial Institutions ◽  
Clustering Algorithm ◽  
Development Trend ◽  
Research Field ◽  
Trend Prediction ◽  
Blockchain Technology ◽  
Supply Chain Finance ◽  
The Government

Blockchain technology is the most cutting-edge technology in the field of financial technology, which has attracted extensive attention from governments, financial institutions and investors of various countries. Blockchain and finance, as an interdisciplinary, cross-technology and cross-field topic, has certain limitations in both theory and application. Based on the bibliometrics data of Web of Science, this paper conducts data mining on 759 papers related to blockchain technology in the financial field by means of co-word analysis, bi-clustering algorithm and strategic coordinate analysis, so as to explore hot topics in this field and predict the future development trend. The experimental results found ten research topics in the field of blockchain combined with finance, including blockchain crowdfunding, Fintech, encryption currency, consensus mechanism, the Internet of Things, digital financial, medical insurance, supply chain finance, intelligent contract and financial innovation. Among them, blockchain crowdfunding, Fintech, encryption currency and supply chain finance are the key research directions in this research field. Finally, this paper also analyzes the opportunities and risks of blockchain development in the financial field and puts forward targeted suggestions for the government and financial institutions.

scholarly journals Underwater Acoustic Target Recognition Based on Depthwise Separable Convolution Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1429
Author(s):  
Gang Hu ◽  
Kejun Wang ◽  
Liangliang Liu
Keyword(s):  
Feature Extraction ◽  
Target Recognition ◽  
Target Feature ◽  
Underwater Acoustic ◽  
Radiated Noise ◽  
Automatic Feature Extraction ◽  
Acoustic Target ◽  
And Cluster Analysis ◽  
Underwater Target ◽  
The One

Facing the complex marine environment, it is extremely challenging to conduct underwater acoustic target feature extraction and recognition using ship-radiated noise. In this paper, firstly, taking the one-dimensional time-domain raw signal of the ship as the input of the model, a new deep neural network model for underwater target recognition is proposed. Depthwise separable convolution and time-dilated convolution are used for passive underwater acoustic target recognition for the first time. The proposed model realizes automatic feature extraction from the raw data of ship radiated noise and temporal attention in the process of underwater target recognition. Secondly, the measured data are used to evaluate the model, and cluster analysis and visualization analysis are performed based on the features extracted from the model. The results show that the features extracted from the model have good characteristics of intra-class aggregation and inter-class separation. Furthermore, the cross-folding model is used to verify that there is no overfitting in the model, which improves the generalization ability of the model. Finally, the model is compared with traditional underwater acoustic target recognition, and its accuracy is significantly improved by 6.8%.

Multi-AUV Underwater Cooperative Search Algorithm based on Biological Inspired Neurodynamics Model and Velocity Synthesis

2015 ◽  
Vol 68 (6) ◽  
pp. 1075-1087 ◽  
Author(s):  
Xiang Cao ◽  
Daqi Zhu
Keyword(s):  
Autonomous Underwater Vehicle ◽  
Search Algorithm ◽  
Integrated Approach ◽  
Underwater Vehicle ◽  
Ocean Currents ◽  
Ocean Current ◽  
Cooperative Search ◽  
Search Path ◽  
Negative Effect ◽  
Underwater Target

Ocean currents impose a negative effect on Autonomous Underwater Vehicle (AUV) underwater target searches, which lengthens the search paths and consumes more energy and team effort. To solve this problem, an integrated algorithm is proposed to realise multi-AUV cooperative search in dynamic underwater environments with ocean currents. The proposed integrated algorithm combines the Biological Inspired Neurodynamics Model (BINM) and Velocity Synthesis (VS) method. Firstly, the BINM guides a team of AUVs to achieve target search in underwater environments; BINM search requires no specimen learning information and is thus easier to apply to practice, but the search path is longer because of the influence of ocean current. Next the VS algorithm offsets the effect of ocean current, and it is applied to optimise the search path for each AUV. Lastly, to demonstrate the effectiveness of the proposed integrated approach, simulation results are given in this paper. It is proved that this integrated algorithm can plan shorter search paths and thus the energy consumption is lower compared with BINM.

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