SAR image target detection method in complex scene

Abstract

The invention discloses an SAR image target detection method in a complex scene, wherein the method comprises the steps: feature training of an SAR image is executed through a YOLOv5 network model, thereby obtaining the initial features of a target in different scenes; a mixed attention module composed of space attention and channel attention is employed to strengthen the initial features, thereby obtaining a feature map with a screening weighting characteristic; for important target features, the network allocates a larger processing weight, so that the feature learning ability of the network to a target region is enhanced, and the target detection rate of a complex scene region is improved; finally, the target position is predicted on the enhanced feature map, the training loss function is optimized, the confidence coefficient of a prediction frame is improved, model parameters are reversely updated, the prediction frame output by the network is closer to a real target, and meanwhile the convergence speed of model updating is increased. According to the method, the SAR image target detection performance in a complex scene is improved, and the SAR image target in any scene can be rapidly detected.

Description

technical field [0001] The invention belongs to the technical field of radar image processing, in particular to a SAR image target detection method in complex scenes. Background technique [0002] In sea surface monitoring, target detection is an extremely important part. Whether it is offshore defense or far sea defense, it is necessary to detect the location of specific targets, so as to improve tactical deployment and enhance coastal defense early warning capabilities. With the continuous development of synthetic aperture radar high-resolution imaging technology, a large number of SAR images can be used for marine ship detection, and SAR image target detection has become an important way to monitor the sea. [0003] At present, the mainstream SAR image target detection methods can be divided into three types macroscopically, including traditional model measurement statistics, machine learning feature classification and deep network learning model. With the advantages of...

AI Technical Summary

Problems solved by technology

With the advantages of high precision, high efficiency, and high robustness, deep learning has been widely used in SAR image target detection. Although the SAR image target detection technology based on deep learning has In the background SAR image, it is difficult to extract target features, and offshore SAR images are easily affected by coastal ports, land, islands, etc., resulting in a certain bottleneck in offshore target detection.

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