Robust sonar image generation method based on conditional double-branch attention mechanism

Abstract

The invention discloses a robust sonar image generation method based on a conditional double-branch attention mechanism. According to the invention, sonar image simulation imaging is carried out on a complex underwater environment by using a deep learning technology. According to the invention, the limitation that a traditional sonar simulation technology pays attention to physical modeling of a bottom layer and is poor in image adjustability and low in fidelity under the conditions of multiple categories and multiple backgrounds is broken through. According to the method, the generation of the sonar image under the specific condition can be effectively controlled by utilizing the condition information, the attention operation of the channel level and the pixel level is sequentially completed through a double-branch attention feature fusion mechanism, and the correlation between corresponding elements is enhanced, so that the clearer and more vivid sonar image is generated. Experiments show that the method has good performance in sonar image simulation and has a robust imaging effect in a noise interference environment, the feasibility of the deep learning method in sonar image simulation is explained, and a new research means is provided for image simulation in 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 robust sonar image generation method based on conditional dual-branch attention generation confrontation network Background technique [0002] In recent years, with the great progress of modern underwater acoustic signal processing technology and underwater acoustic equipment research and development technology, underwater sonar image generation technology has become a research hotspot at home and abroad. Sonar image simulation technology plays an important role in both military and civilian fields. Especially in the military field, the detection and identification of enemy military targets (submarines, torpedoes, dangerous obstacles) in complex environments, and submarine terrain matching navigation urgently need the application of high-fidelity sonar image simulation technology. [0003] Sonar image...

AI Technical Summary

Problems solved by technology

In order to improve the efficiency and fidelity of sonar data simulation in complex environments, and better solve the problems of low intelligence and low fidelity of simulation images in traditional sonar data simulation technology, the present invention proposes a condition-based dual-branch attention Generative Adversarial Networks for Robust Sonar Image Generation

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