Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

Wang, Xingmei; Liu, Anhua; Xue, Fuzhao

doi:10.3390/rs11161888

Cited by 65 publications

(19 citation statements)

References 33 publications

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“…By establishing an artificial neural network with a hierarchical structure, artificial intelligence is realized in the computing system. The artificial intelligence target detection technology of images refers to the application of techniques such as machine vision to determine the type, position, size, and confidence of the target object, and the predetermined target object is automatically detected from the image (Wang et al, 2019). The basic process of target detection technology is shown in Figure 1.…”

Section: Proposed Methods Target Detection Technology Based On Deep Lementioning

confidence: 99%

Target Recognition of Industrial Robots Using Machine Vision in 5G Environment

et al. 2021

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The purpose is to solve the problems of large positioning errors, low recognition speed, and low object recognition accuracy in industrial robot detection in a 5G environment. The convolutional neural network (CNN) model in the deep learning (DL) algorithm is adopted for image convolution, pooling, and target classification, optimizing the industrial robot visual recognition system in the improved method. With the bottled objects as the targets, the improved Fast-RCNN target detection model's algorithm is verified; with the small-size bottled objects in a complex environment as the targets, the improved VGG-16 classification network on the Hyper-Column scheme is verified. Finally, the algorithm constructed by the simulation analysis is compared with other advanced CNN algorithms. The results show that both the Fast RCN algorithm and the improved VGG-16 classification network based on the Hyper-Column scheme can position and recognize the targets with a recognition accuracy rate of 82.34%, significantly better than other advanced neural network algorithms. Therefore, the improved VGG-16 classification network based on the Hyper-Column scheme has good accuracy and effectiveness for target recognition and positioning, providing an experimental reference for industrial robots' application and development.

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Section: Proposed Methods Target Detection Technology Based On Deep Lementioning

confidence: 99%

Target Recognition of Industrial Robots Using Machine Vision in 5G Environment

et al. 2021

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“…As such, it is a much less clear problem than supervised learning, and there is no clear error metric. In underwater acoustics, a considerable number of previous sonar application studies have used machine learning for classification purposes, such as target type/state classification (Choi et al, 2019;Fischell and Schmidt, 2015;Ke et al, 2018;Wang et al, 2019) and target and clutter signal classification (Allen et al, 2011;Murphy and Hines, 2014;Young and Hines, 2007). In many of these studies, the properties of the data that were used for learning were recognized beforehand owing to the goals of the studies.…”

Section: Definitions Types and Basic Concepts Of Machine Learningmentioning

confidence: 99%

Underwater Acoustic Research Trends with Machine Learning: General Background

Yang

Lee

Choo

et al. 2020

J. Ocean Eng. Technol.

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Underwater acoustics is the study of all phenomena related to the occurrence, propagation, and reception of sound waves in the water medium. Because electromagnetic waves undergo a significant attenuation in water, sound waves, which have relatively low propagation loss and high propagation speed, are used for underwater communication and detection. In the field of underwater acoustics, studies are mainly conducted on underwater communications, underwater target detection, marine resources, and measurement and analysis of underwater sound sources. Most applications for underwater acoustics can be described as remote sensing. Remote sensing is employed when an object, condition, or phenomenon of interest cannot be directly observed and information about the target of interest is acquired indirectly using data. In underwater acoustics, this can be described simply as a sound navigation and ranging (sonar) system. Sonar systems can be broadly classified into passive and active systems. Passive sonar systems acquire information by using sensors to measure the acoustic energy (signal) emitted by the target of interest. In active sonar systems, the observer obtains information by directly emitting an acoustic pulse and gathering the returning signals that are reflected by the target. Machine learning, which is widely known today, was initially used in academia for developing artificial intelligence. Recently, the use of machine learning has become widespread owing to the introduction of high-speed parallel computing that uses graphics processing units (GPUs) and can perform reliable learning based on big data, as well as develop various machine learning techniques that can find optimal solutions. Machine learning has contributed to the evolution of acoustic signal processing and voice recognition, and it is also utilized in various ways in the field of underwater acoustics. It is used for traditional remote sensing, such as in detection/classification of underwater sound sources and targets and localization. In addition, it is being used in the field of acoustic signal processing for seabed classification and marine environment information extraction and is producing an abundance of scientific results. Data-driven machine learning divides the data into a training set and test set. The training set is used to create a model that is suitable for machine learning, and the model's accuracy is increased through a repetitive model update process in which the model is validated via the

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“…Feature extraction is the process of obtaining features from the original signal. Commonly used algorithms include traditional time-frequency graph methods [ 3 ], auditory perception methods [ 4 , 5 ], and multi-dimensional feature fusion methods [ 6 ]. Pattern recognition algorithm is to divide the samples into certain categories according to the characteristics of the samples.…”

Section: Introductionmentioning

confidence: 99%

An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine

Luo

Feng

2020

Sensors

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This article focuses on an underwater acoustic target recognition method based on target radiated noise. The difficulty of underwater acoustic target recognition is mainly the extraction of effective classification features and pattern classification. Traditional feature extraction methods based on Low Frequency Analysis Recording (LOFAR), Mel-Frequency Cepstral Coefficients (MFCC), Gammatone-Frequency Cepstral Coefficients (GFCC), etc. essentially compress data according to a certain pre-set model, artificially discarding part of the information in the data, and often losing information helpful for classification. This paper presents a target recognition method based on feature auto-encoding. This method takes the normalized frequency spectrum of the signal as input, uses a restricted Boltzmann machine to perform unsupervised automatic encoding of the data, extracts the deep data structure layer by layer, and classifies the acquired features through the BP neural network. This method was tested using actual ship radiated noise database, and the results show that proposed classification system has better recognition accuracy and adaptability than the hand-crafted feature extraction based method.

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Underwater Acoustic Target Recognition: A Combination of Multi-Dimensional Fusion Features and Modified Deep Neural Network

Cited by 65 publications

References 33 publications

Target Recognition of Industrial Robots Using Machine Vision in 5G Environment

Target Recognition of Industrial Robots Using Machine Vision in 5G Environment

Underwater Acoustic Research Trends with Machine Learning: General Background

An Underwater Acoustic Target Recognition Method Based on Restricted Boltzmann Machine

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