Underwater Internet of Things-Based Solutions for Intelligent Marine Target Recognition

Abstract

In order to solve the problems of poor data processing ability of underwater hardware equipment and low accuracy of classification algorithms in the existing marine target recognition and detection methods based on sensors and transducers, by combining perception technology, underwater Internet of Things technology, and artificial intelligence, multiple devices could communicate with each other to achieve automatic and intelligent high-precision marine target recognition. Compared with existing methods, not only the accuracy rate is improved but also the hardware requirements are lower, and it is easier to deploy in engineering.

Evolving LSTM Networks for Time‐Series Classification in EdgeIoT

“…Te model uses LSTM to capture long-term time dependencies and uses a convolutional network to obtain the features. For the problem of intelligent marine target recognition in the UIoT systems, an evolutionary LSTM framework, using exploration and exploitation control method, is proposed in [16]. A novel method called evolving deep convolutional variational autoencoder is proposed for image classifcation in [17], a gene-coding mechanism with variable length is proposed to fnd the optimal network depth.…”