Sonar Image Target Detection and Recognition Based on Convolution Neural Network

Author:

Yanchen Wu1ORCID

Affiliation:

1. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

Abstract

Recent advancements in deep learning offer an effective approach for the study in machine vision using optical images. In this paper, a convolution neural network is used to deal with the target task of sonar detection, and the performance of each neural network model in the sonar image detection and recognition task of underwater box and tire is compared. The simulation results show that the neural network method proposed in this paper is better than the traditional machine learning methods and SSD network models. The average accuracy of the proposed method for sonar image target recognition is 93%, and the detection time of a single image is only 0.3 seconds.

Publisher

Hindawi Limited

Subject

Computer Networks and Communications,Computer Science Applications

Reference27 articles.

1. Target detection in SAR image based on convolutional neural network;G. Li;National Security Geophysics Professional Committee of Chinese Geophysical,2020

2. High resolution optical remote sensing image target detection based on rotation invariant convolution neural network;L. Cheng;Scientific Observation,2020

3. Aircraft target detection in remote sensing images based on the deformable convolutional neural network;L. M. Yang;Foreign Electronic Measurement Technology,2020

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