Infrared target instance segmentation method based on feature fusion and a dense connection network

A technology of feature fusion and network connection, applied in the field of image processing, it can solve the problems of insensitive details, smoothness, blurring, etc., and achieve the effect of solving gradient explosion/gradient disappearance, good robustness and generalization, and improving accuracy.

Pending Publication Date: 2019-04-05
XIDIAN UNIV
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Problems solved by technology

However, the upsampling results used by the network are still blurry

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  • Infrared target instance segmentation method based on feature fusion and a dense connection network
  • Infrared target instance segmentation method based on feature fusion and a dense connection network
  • Infrared target instance segmentation method based on feature fusion and a dense connection network

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Embodiment Construction

[0042] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0043] Embodiments of the present invention provide a method for instance segmentation of infrared surface targets based on feature fusion and densely connected networks, such as figure 1 , 2 As shown, the method is implemented through the following steps:

[0044] Step 1 Build the training set

[0045] Collect and construct the infrared image data set required for instance segmentation, calibrate the pixel-level outline, category information and target frame position of the required segmentation target in the infrared image data set containing the required segmentation target, and obtain the ori...

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Abstract

The invention discloses an infrared target instance segmentation method based on feature fusion and a dense connection network, and the method comprises the steps: collecting and constructing an infrared image data set required for instance segmentation, and obtaining an original known infrared tag image; Performing image enhancement preprocessing on the infrared image data set; Processing the preprocessed training set to obtain a classification result, a frame regression result and an instance segmentation mask result graph; Performing back propagation in the convolutional neural network by using a random gradient descent method according to the prediction loss function, and updating parameter values of the convolutional neural network; Selecting a fixed number of infrared image data training sets each time and sending the infrared image data training sets to the network for processing, and repeatedly carrying out iterative updating on the convolutional network parameters until the convolutional network training is completed by the maximum number of iterations; And processing the test set image data to obtain average precision and required time of instance segmentation and a finalinstance segmentation result graph.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to an infrared surface target instance segmentation method based on feature fusion and dense connection network. Background technique [0002] The target instance segmentation is to give each pixel in the image the instance category of the pixel, and predict the category label and pixel-level instance mask to locate different numbers of instances in the image. Instance segmentation of an image can obtain the target information of the image, and better understand the high-level content and representative information of the image. Instance segmentation is one of the most important and challenging tasks. The positioning of specific targets in the image, image It is very useful for search, recognition of road scenes in automatic driving, and video surveillance, and has high practical value in practical applications. The existing basic ideas of instance segmentation...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06N3/04G06N3/08
CPCG06N3/084G06T7/11G06T2207/20104G06N3/045Y02T10/40
Inventor 周慧鑫周腾飞张喆赵东宋江鲁奇秦翰林于跃李欢赖睿黄楙森杜娟宋尚真姚博
Owner XIDIAN UNIV
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