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Image Accurate Segmentation Method Combining Deep Learning Network and Watershed Algorithm

A deep learning network, watershed algorithm technology, applied in image analysis, image data processing, computing and other directions, to achieve the effect of improving detection accuracy

Active Publication Date: 2021-02-12
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] In order to solve the problem of accurate segmentation in the background technology, the present invention provides an image accurate segmentation method that integrates the deep learning network and the watershed algorithm, and fine-segments the area around the initial segmentation result, which can improve the image segmentation accuracy

Method used

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  • Image Accurate Segmentation Method Combining Deep Learning Network and Watershed Algorithm
  • Image Accurate Segmentation Method Combining Deep Learning Network and Watershed Algorithm
  • Image Accurate Segmentation Method Combining Deep Learning Network and Watershed Algorithm

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Embodiment Construction

[0034]The present invention will be further described below in conjunction with the drawings and specific embodiments.

[0035]The specific embodiments of the present invention are as follows:

[0036]A video camera (in this example, DS-2CD3T20-I3) and a hard disk video recorder (in this example, ST4000VX000) are used to continuously shoot and record images of multiple gestational sows.

[0037]Step 1: Select 1000 sow images of different scenes, time periods and shooting angles, and use common image processing software to perform image recognition to obtain the sow contour, and turn the area outside the sow contour into black as a data set.

[0038]Step 2: Randomly pick 124 images from the data set as the test set, and use the remaining 876 images as the training set, and use DeepLab for model training to obtain the sow recognition model.

[0039]Step 3: Pending images such asfigure 1 As shown, the image to be determined is recognized by the sow recognition model, and an initial segmentation image...

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Abstract

The invention discloses an image accurate segmentation method integrating a deep learning network and a watershed algorithm. The DeepLab recognition model is used to identify the to-be-determined image to obtain the initial segmentation map, and the watershed algorithm is used to segment the to-be-determined image to obtain a set of undetermined areas, and the number of undetermined areas is multiplied by the initial segmentation map, and the undetermined area is divided into objects to be tested area, otherwise the undetermined area in the area of ​​the object to be tested will be removed. The invention comprehensively utilizes the distance between the to-be-determined point and the material center to be measured and the gray-scale difference between the to-be-determined point and the foreground and background to judge the attributes of the equal points, thereby realizing the accurate segmentation of the image. The invention utilizes the characteristic of dividing adjacent pixels with similar gray levels by watershed, adopts the deep learning method, establishes the core area of ​​the object to be tested, and improves the detection accuracy.

Description

Technical field[0001]The invention relates to a method for further improving the accuracy of image segmentation on the basis of image segmentation in the prior art, in particular to a method for precise image segmentation combining a deep learning network and a watershed algorithm.Background technique[0002]Image segmentation is the process of dividing an image into multiple specific areas to represent different things. It is an important step in target recognition.[0003]In pig behavior detection, pigs need to be identified from various backgrounds to realize pig image segmentation and lay the foundation for further behavior analysis. However, due to the existence of various facilities in the pig farm and the continuous change of lighting conditions, traditional image segmentation methods are easy to fail.[0004]In recent years, deep learning methods have been applied in image segmentation.[0005]FCN is the earliest classic model of image segmentation (LONG J, SHELHAMER E, DARRELL T. F...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/12G06T7/187
Inventor 饶秀勤宋晨波张小敏高迎旺应义斌泮进明郑荣进
Owner ZHEJIANG UNIV
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