Sonar target detection method based on Faster R-CNN

A target detection and sonar technology, which is applied in the field of three-dimensional imaging sonar target detection based on deep learning, can solve the problems of low signal-to-noise ratio of three-dimensional imaging sonar images, cumbersome and complicated detection processing process, false detection or missed detection of targets, etc. Speed ​​and Efficiency Enhancements

Inactive Publication Date: 2018-09-28
HANGZHOU DIANZI UNIV
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Problems solved by technology

[0004] Although the methods of underwater target detection are constantly innovating and improving, it is found through investigation and research on underwater target detection and recognition methods in recent years that the current underwater target detection and recognition methods still face many problems in the feature extraction of targets. bottleneck probl

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  • Sonar target detection method based on Faster R-CNN
  • Sonar target detection method based on Faster R-CNN
  • Sonar target detection method based on Faster R-CNN

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Embodiment

[0052] Step (1), 3D imaging sonar data preprocessing

[0053] For the sonar data collected by 3D imaging sonar, through a specific protocol format, use MATLAB to write a batch analysis program to parse the sonar image from the original sonar data for subsequent feature extraction.

[0054] Parsing the sonar image from the original sonar data belongs to the existing mature technology, so it will not be explained in detail.

[0055] Step (2), feature extraction of sonar images

[0056] The present invention adopts regional accelerated convolutional neural network (Faster-RCNN) to carry out feature extraction to sonar image target:

[0057] 2.1. Extract the sonar feature map through a shared convolutional neural network (Convolution Neural Network, CNN); wherein the sonar image obtained in step (1) preprocessing is used as the input of CNN;

[0058] The specific process of extracting the sonar feature map is:

[0059] 2.1.1 Scale the sonar images of different sizes obtained by...

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Abstract

The invention discloses a sonar target detection method based on the Faster R-CNN. According to the method, characteristic extraction is performed on targets in different complex underwater environments through utilizing the deep learning technology, the method is the major innovation and attempt compared with present sonar target detection methods, limitations of traditional methods are broken, deep characteristics of sonar images at the low SNR can be extracted, and good target detection and identification on the linear targets can be carried out. The sonar target detection and identification network based on the regional accelerated convolutional neural network (Faster-RCNN) is established to carry out target detection for sonar data. The method is advantaged in that good performance oflinear target detection of the sonar images is achieved, feasibility of the deep learning method in sonar target detection is verified, and a new research method is provided for characteristic extraction of the complex underwater acoustic environment data.

Description

technical field [0001] The invention belongs to the intersection field of artificial intelligence and underwater acoustic electronic information, and specifically relates to a three-dimensional imaging sonar target detection method based on deep learning. Background technique [0002] With the rapid development of information technology, underwater detection technology has been greatly promoted, and its application fields are becoming more and more extensive: in military applications, it can be used to observe underwater topography, detect torpedoes, underwater weapon guidance and countermeasures, and search for submarines , underwater navigation, seabed warning, etc.; in civil and commercial applications, marine resource detection can be carried out. [0003] Underwater target detection and recognition has become a research hotspot in the coastal defense of various countries, and it provides technical support for marine combat and operation capabilities. Underwater target ...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08G01S15/89
CPCG06N3/08G01S15/89G06N3/045G06F2218/08G06F2218/12
Inventor 孔万增陈威于金帅范巧男王楼
Owner HANGZHOU DIANZI UNIV
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