Underwater target sonar detection method based on CNN

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 eliminate The effect of sidelobe effects

Active Publication Date: 2021-04-30
SHANGHAI UNIV
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  • Description
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  • 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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  • Underwater target sonar detection method based on CNN
  • Underwater target sonar detection method based on CNN
  • Underwater target sonar detection method based on CNN

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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 an underwater target sonar detection method based on a CNN. The method specifically comprises the following steps of: S1, carrying out 360-degree sonar data collection on an underwater target through an active sonar technology to obtain original sonar data; S2, preprocessing the original sonar 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, training a CNN network model by using the training set; and S5, inputting the test set into the trained CNN model for testing to obtain a detection and recognition result. According to the method, a convolutional neural network with a single convolutional layer is adopted, a nonlinear excitation function Sigmoid is used during model training and is replaced with Softplus during actual testing, so that the signal intensity of the detection target is better displayed 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 Applications(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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