A CNN-based sonar detection method for underwater targets

A technology for underwater targets and detection methods, applied in neural learning methods, measuring devices, and re-radiation of sound waves, can solve the problems of low accuracy and detection speed, simplify training, display the signal strength of detection targets, and improve The effect of precision

Active Publication Date: 2021-11-05
SHANGHAI UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the target detection based on traditional median filtering, mean filtering and other image processing methods, its accuracy and detection rate are not high

Method used

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  • A CNN-based sonar detection method for underwater targets
  • A CNN-based sonar detection method for underwater targets
  • A CNN-based sonar detection method for underwater targets

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Embodiment 1

[0038] In order to identify the ship target in the 360° sonar image acquired by the submarine through active sonar technology detection underwater, the present invention introduces and improves the convolutional neural network, detects the ship target in a simple and efficient way, and simplifies the neural network model The training process improves the underwater target recognition rate, and is suitable for detecting and identifying underwater targets in low-brightness, low-noise and complex submarine environments.

[0039] refer to figure 1 Shown, method of the present invention specifically comprises the following steps:

[0040] S1: By deploying sonar equipment around the submarine and using active sonar technology, the 360° sonar data is collected and the original sonar data is obtained.

[0041] S2: Preprocessing the original sonar data. Use MATLAB software to write a batch analysis program to analyze the sonar image from the original sonar data, so as to facilitate s...

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Abstract

The invention discloses a CNN-based sonar detection method for an underwater target, which specifically includes the following steps: S1: collecting 360° sonar data of the underwater target through active sonar technology to obtain original sonar data; S2: analyzing the original sonar Preprocessing the data to obtain a sonar image; S3: expanding the sonar image to obtain a sonar image data set; and dividing the sonar image data set into a training set and a test set; S4: using the training set to CNN The network model is trained; S5: Input the test set into the trained CNN model for testing to obtain detection and recognition results. The present invention adopts a convolutional neural network with a single-layer convolution layer, uses a non-linear excitation function Sigmoid when training the model, and replaces it with Softplus when actually testing, and better displays the signal strength of the detection target on the basis of simplified training.

Description

technical field [0001] The invention relates to the technical field of intersection of underwater target recognition and artificial intelligence, in particular to a CNN-based sonar detection method for underwater targets. Background technique [0002] Sonar is a working method that transmits sound waves and then receives sound waves. It is often used to complete tasks such as underwater information transmission and detection of underwater targets. In recent years, applications and tools using underwater target recognition technology have emerged in an endless stream at home and abroad. For example, in the military field, target detection technology is used to identify underwater targets such as torpedoes and submarines. It can be seen that underwater target detection and recognition has now become a research hotspot in the coastal defense and industrial fields of various countries in the world. [0003] The artificial neural network is a research hotspot emerging in the fi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S15/89G06K9/62G06N3/04G06N3/08
CPCG01S15/89G06N3/08G06N3/048G06N3/045G06F18/241
Inventor 张亦弛曾丹冯小予张钟浩
Owner SHANGHAI UNIV
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