A ship target detection method based on the fusion of CFAR and Fast-RCNN in SAR image

A target detection and ship detection technology, applied in the field of target detection, can solve the problem of reducing the number of suggestion boxes and achieve the effect of improving the utilization rate

Active Publication Date: 2019-01-04
BEIJING INST OF REMOTE SENSING EQUIP
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The suspected target points are obtained through the CFAR algorithm, and then the candidate suggestion frame is obtained through the morphological filtering of the combination of mult

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  • A ship target detection method based on the fusion of CFAR and Fast-RCNN in SAR image
  • A ship target detection method based on the fusion of CFAR and Fast-RCNN in SAR image
  • A ship target detection method based on the fusion of CFAR and Fast-RCNN in SAR image

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specific Embodiment approach

[0031] In this embodiment, the SAR image ship target detection method based on the fusion of CFAR and Fast-RCNN includes the following steps:

[0032] (1) Candidate suggestion frame extraction: Obtain the suspected target points of the original image through the CFAR algorithm, specifically, first sample the high-resolution image to a low-resolution (wherein, the high-resolution is figure 2As shown, the sliding window includes a target window, a protection window and a background window. If the average brightness value of the pixels of the target window is greater than K times the average value of the background window, the center point corresponding to the window is considered to be the target point and is set to 1, otherwise set to is 0; the size of the target window is set to the size of the small boat under the condition of 5m resolution, the size of the protection window is set to twice the size of the large ship under the condition of 5m resolution, and the length of each...

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Abstract

The invention discloses a method for SAR image Ship target detection based on based on CFAR and Fast-RCNN fusion. The method is based on Fast-RCNN Target Detection Framework, and based on the featurethat the brightness of ship target is higher than that of background in SAR image, and comprises steps: at first, the suspect target points are obtained by a CFAR algorithm, then morphological filtering based on multiple groups of structural elements is performed to obtain candidate suggestion boxes, which not only reduces the number of candidate regions and improves the efficiency of the algorithm, but also ensures the effectiveness of the suggestion boxes, and overcomes the shortcomings that the same target is divided into multiple parts and the similar targets are mistaken as the same target by using a single filtering factor group; on the basis of the extracted candidate suggestion boxes, Fast-RCNN network model training is completed, and based on the model to achieve any input image of the target classification and calibration box regression, through the use of CNN feature extraction network, the shortcomings of manual feature extraction are overcome, and the data utilization is improved.

Description

technical field [0001] The invention belongs to the field of target detection and relates to a ship target detection algorithm based on CFAR and Fast-RCNN fusion of SAR images. Background technique [0002] Ship detection plays a prominent role in national marine security, marine management, monitoring illegal fishing, etc. Radar detection has the characteristics of all-day and all-weather. With the advancement of SAR imaging technology, the resolution of SAR images has been continuously improved and the amount of information has become more and more abundant. Ship target detection based on SAR images has become a major research hotspot today. Based on the remarkable feature that the ship target in the SAR image is brighter than the background, most of the ship target detection algorithms in the SAR image usually use the target detection method based on CFAR to obtain the candidate area, and then extract the target area through manual feature extraction combined with machin...

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Application Information

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IPC IPC(8): G06K9/00G06K9/40G06N3/04
CPCG06V20/13G06V10/30G06N3/045
Inventor 杨小婷何向晨李洪鹏房嘉奇
Owner BEIJING INST OF REMOTE SENSING EQUIP
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