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High-speed and high-precision SAR image ship detection method

A ship detection, high-precision technology, applied in the field of synthetic aperture radar image interpretation, can solve the problems of high detection accuracy and slow detection speed

Active Publication Date: 2020-10-20
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] Therefore, in order to solve the problem of high precision but slow detection speed of traditional SAR ship detection, the present invention proposes a high-speed and high-precision SAR image ship detection method based on the target detection theory of deep learning

Method used

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  • High-speed and high-precision SAR image ship detection method
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  • High-speed and high-precision SAR image ship detection method

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Embodiment Construction

[0151] Step 1. Prepare the dataset

[0152] Download the SSDD data set from the link https: / / pan.baidu.com / s / 1dF6g3ZF in the definition 1 literature, the Gaofen-SSDD data set in the definition 2 and the Sentinel-SSDD data set in the definition 3 can be obtained from the link https: / / github.com / CAESAR-Radi / SAR-Ship-Dataset is downloaded and obtained, and these data sets are mixed in random order to obtain a larger new data set, which is recorded as SSDD_new;

[0153] The SSDD_new data set is randomly divided according to the ratio of 7:2:1. The data sets contained in each ratio correspond to the training set, verification set and test set respectively, and the training set is recorded as Train, the verification set is recorded as Val, and the test set is recorded as Record it as Test.

[0154] Using the YOLOv3 data format under the standard Keras framework in Definition 4, adjust the data format in Train, Val and Test, and finally get a new training set, a new verification se...

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Abstract

The invention discloses a high-speed and high-precision SAR image ship detection method. The method is based on a target detection theory of deep learning, and a ship detection model is constructed byusing a deep separation convolutional neural network. The detection model provided by the invention mainly comprises a backbone network and five modules, wherein the backbone network is used for extracting the characteristics of a ship, the five modules are a multi-receptive-field module, a cavity convolution module, a channel and space attention module, a characteristic fusion module and a characteristic pyramid module which are used for improving the detection precision. The method is advantaged in that a model provided by the invention has relatively small depth and width, therefore, the ship detection model has fewer parameters, so the ship detection model provided by the invention has higher detection speed, and the detection speed on an SSDD data set, a Gaofen-SSDD data set and a Sentinel-SSDD data set exceeds 220FPS (FPS is a frame rate).

Description

technical field [0001] The invention belongs to the technical field of synthetic aperture radar (SAR) image interpretation, and relates to a high-speed and high-precision SAR image ship detection method. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution microwave active imaging radar. It has all-weather and all-weather working characteristics. Compared with optical sensors, the electromagnetic waves emitted by SAR can penetrate clouds, vegetation, etc. The occlusion of complex environmental objects, and can not be affected by the light of the detection area, so it has a wide range of applications in the civil and military fields. Maritime targets are a very valuable target in the field of SAR imaging. Synthetic aperture radar can provide data support for fishing, marine traffic, oil spills, ship monitoring and other tasks through the observation of maritime targets. For details, please refer to the literature "Ou Yening. Application Research ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06N3/045G06F18/253
Inventor 张晓玲张天文郑顺心师君韦顺军
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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