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. According to the traditional image processing method, an image segmentation method is used for distinguishing a background from a target; the method comprises the following steps of: 1, establishing a dual-path feature fusion network; and 2, training the dual-path feature fusion network obtained in the step 1; 3, performing sonar image generation and feature extraction; and 4, classifying and detecting the sonar image target frame by combining the default frame. According to the method,the deep learning technology is fused into target detection, the sonar image generated by the sonar data is input into the network model, feature extraction, target detection and target classificationare completed in the model at a time, and therefore the detection speed is greatly increased. According to the method, more deep features can be extracted, and target classification and regression are optimized fundamentally. According to the invention, multi-scale dense connection is adopted to fuse multi-level characteristics and improve the detection effect of medium and small 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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