Segmentation and detection method of remote sensing ship target contour based on deep learning fcn network

A technology of deep learning and detection methods, applied in biological neural network models, image analysis, image enhancement, etc., can solve the problems of high false alarm rate and low detection accuracy

Active Publication Date: 2020-07-07
NO 54 INST OF CHINA ELECTRONICS SCI & TECH GRP
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Problems solved by technology

These methods have low detection accuracy and high false alarm rate.

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  • Segmentation and detection method of remote sensing ship target contour based on deep learning fcn network
  • Segmentation and detection method of remote sensing ship target contour based on deep learning fcn network
  • Segmentation and detection method of remote sensing ship target contour based on deep learning fcn network

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[0039] The specific embodiments of the present invention are described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be particularly reminded that in the following description, when the detailed description of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0040] figure 1 It is the principle block diagram of a specific implementation of the remote sensing ship target detection method based on the deep learning FCN network of the present invention.

[0041] In this embodiment, as figure 1 The remote sensing ship target contour segmentation and detection method based on deep learning FCN network shown includes the following steps:

[0042] 1. Data preparation

[0043] Data preparation includes the establishment of a remote sensing detection target database and the labeling of remote sensing ship targets. In this embo...

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Abstract

The invention discloses a remote sensing ship contour segmentation and detection method based on a deep learning FCN full convolution network. Firstly, a remote sensing ship target database is constructed, and the remote sensing ship targets are marked pixel by pixel. Then the invention designs a deeper The 6-layer fully convolutional network 6-FCN structure performs parameter training through convolution and deconvolution, and finally performs overlapping segmentation on the wide-range remote sensing detection image, and merges after detection to obtain the final remote sensing image ship detection result. It can realize the accurate segmentation of the ship outline while efficiently and quickly realizing the target detection of the remote sensing ship, which simplifies the traditional complex detection process, and achieves a good segmentation and detection effect improvement.

Description

Technical field [0001] The invention belongs to the technical field of remote sensing image intelligent recognition, and more specifically, relates to a remote sensing ship target detection method based on a full convolutional neural network in a monitoring scene. Background technique [0002] Satellite ocean surveillance has the characteristics of wide imaging bandwidth and large amount of data. The focus of military activities is to carry out reconnaissance, tracking and monitoring of maritime moving targets, accumulating and mastering the deployment and target trends of maritime military forces in relevant countries and regions. It is necessary to detect and extract target images quickly and accurately. The use of computer technology to realize remote sensing image remote sensing ship target detection has become a research focus at home and abroad. [0003] The existing ship inspection methods, [0004] (1) Most of them mainly include two-parameter CFAR method and CFAR method bas...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/12G06T7/13G06N3/04
CPCG06T7/12G06T7/13G06T2207/10032G06T2207/20084G06T2207/20081G06T2207/20021G06N3/045
Inventor 楚博策
Owner NO 54 INST OF CHINA ELECTRONICS SCI & TECH GRP
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