Robust sonar target detection method based on dual-path feature fusion network

Abstract

The invention discloses a robust sonar target detection method based on a dual-path feature fusion network. The traditional image processing method uses the method of image segmentation to distinguish the background from the target; the present invention is as follows: 1. Build a dual-path feature fusion network. 2. Train the dual-path feature fusion network obtained in step 1. 3. Sonar image generation and feature extraction. Fourth, classify and detect the target frame of the sonar image in combination with the default frame. The present invention integrates deep learning technology into target detection, inputs sonar images generated by sonar data into a network model, and completes feature extraction, target detection, and target classification in the model at one time, thereby greatly improving the detection speed. The present invention can extract more deep-level features, and fundamentally optimizes target classification and regression. The present invention adopts multi-scale dense connection to fuse multi-level features and improve the detection effect of small and medium targets.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and underwater acoustic electronic information, and in particular relates to a robust sonar target detection method based on a dual-path feature fusion network. Background technique [0002] With the continuous iteration and development of computer science and technology, underwater detection technology has been greatly promoted in recent years, and has a wide range of applications in military and civil fields, such as military confrontation, dangerous target screening, target tracking, etc.; in other The field also plays an important role in underwater rescue, seabed resource exploration, tracking and protection of endangered species, and seabed modeling. [0003] Underwater target detection and recognition is an important part of modern sonar systems and underwater acoustic countermeasures. It is the research focus of each country's maritime security. It has been widely concerned...

AI Technical Summary

Problems solved by technology

[0009] The contrast and signal-to-noise ratio are low, and they are susceptible to formation noise interference. In this case, the current underwater target detection and recognition methods still have many bottlenecks, such as incomplete or slow extraction of target features in sonar images, and small underwater targets. Due to low contrast and signal-to-noise ratio, it is falsely detected or missed, and it is impossible to achieve high precision, strong robustness, and system real-time performance at the same time. Therefore, sonar target detection and recognition methods also need continuous development and innovation

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