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Remote sensing ship contour segmentation and detection method based on deep learning full convolution network (FCN)

A technology of deep learning and target contour, applied in biological neural network models, image analysis, image data processing, etc., can solve the problems of high false alarm rate and low detection accuracy, achieve the goal of streamlining redundant processes and overcoming edge effects Effect

Active Publication Date: 2017-12-29
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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  • Remote sensing ship contour segmentation and detection method based on deep learning full convolution network (FCN)
  • Remote sensing ship contour segmentation and detection method based on deep learning full convolution network (FCN)
  • Remote sensing ship contour segmentation and detection method based on deep learning full convolution network (FCN)

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Embodiment Construction

[0039] Specific embodiments of the present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when detailed descriptions 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 a functional 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 example, if figure 1 The shown remote sensing ship target contour segmentation and detection method based on deep learning FCN network includes the following steps:

[0042] 1. Data preparation

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

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Abstract

The invention discloses a remote sensing ship contour segmentation and detection method based on a deep learning full convolution network (FCN). The method comprises steps of: firstly constructing a remote sensing ship target database, marking a remote sensing ship target pixel by pixel; then designing a deeper 6-layer full convolution network (6-FCN) structure to perform parameter training by convolution and deconvolution; and finally, subjecting a wide remote sensing detection image to overlapped segmentation, and merging to obtain a final remote sensing image ship detection result after detection. The method can accurately segment the ship contour while effectively and rapidly realizing remote sensing ship target detection, simplifies a traditional complex detection process, and achieve a good segmentation and detection effect.

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 fully 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 moving targets at sea, and to accumulate and grasp the deployment and target trends of relevant countries and regions' maritime military forces. It is very necessary to quickly and accurately detect and extract target images, and the use of computer technology to realize remote sensing ship target detection in remote sensing images has become a research focus at home and abroad. [0003] The existing ship detection methods, [0004] (1) Most of them mainly include two-parameter CFAR method,...

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Application Information

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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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