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Feasible region detecting method based on machine learning

A technology of area detection and machine learning, which is applied to instruments, computer parts, image data processing, etc., can solve the problem of detection of feasible areas of unmanned platforms

Active Publication Date: 2015-07-15
ZHEJIANG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a feasible region detection method based on machine learning, which mainly solves the problem of feasible region detection of unmanned platforms

Method used

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  • Feasible region detecting method based on machine learning
  • Feasible region detecting method based on machine learning
  • Feasible region detecting method based on machine learning

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Embodiment Construction

[0049] The present invention uses the unmanned platform provided with camera equipment as a carrier, and implements through the following steps:

[0050] A method for detecting a feasible region based on machine learning, wherein the method uses an unmanned platform provided with camera equipment as a carrier to detect a feasible region, comprising the steps of:

[0051] Step 1. Define the value w, which represents the image width value in the image, and the number of matrix columns in the matrix; define the value h, represents the image height in the image, and represents the number of matrix rows in the matrix; set the image width to w, and the image height For h, construct an image coordinate system; determine five point coordinates according to the height and angle of the unmanned platform, including (x1, y1), (x2, 0), (w, y3), (x4, y4), (x5, h ); define FM as a matrix with h rows and w columns, and each element value is only +1, 0 or -1, and each element of the matrix is ...

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Abstract

The invention provides a feasible region detecting method based on machine learning to solve the technical problems. The method comprises steps as follows: parameter determination, feature construction, feature classification, updating of a training dataset and updating of a classifier. The method is implemented through circulatory iteration of the steps, and a feasible region of a whole image can be extracted by continuously expanding a small feasible region. The method is a real-time and effective method in a complicated change scene, and a feasible region in current view can be extracted by the aid of an outdoor unmanned platform on the basis of the method, so that safe and stable operation of the unmanned platform is guaranteed.

Description

technical field [0001] The invention relates to a method for extracting a feasible region of an unmanned platform, in particular to a method for realizing adaptive detection of a feasible region through a visual sensor and a machine learning method. Background technique [0002] Vision-based feasible region detection has made great progress with the improvement of computing unit performance. However, the current method has disadvantages such as poor scene adaptability, poor light adaptability and weak stability, which bring hidden dangers to the stable and safe operation of the unmanned platform. For example, some feasible region extraction methods first require that the feasible region to be detected has a vanishing point, but the situation that there is no vanishing point in the feasible region often occurs when the robot is actually running, for example, in the intersection scene. Even on the straight road, the vanishing point may not appear in the field of view due to t...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06T7/00
Inventor 刘勇张清泉薛睿
Owner ZHEJIANG UNIV