Millimeter wave image target detection method

A target detection, millimeter wave technology, applied in the field of computer vision

Pending Publication Date: 2021-06-15
TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] For this reason, the first purpose of this application is to propose a millimeter wave image target detection method, which solves the technical problem of detection and identification of dangerous objects in millimeter wave images in existing methods, and realizes millimeter wave image target detection

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Embodiment Construction

[0036] Embodiments of the present application are described in detail below, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the figures are exemplary, and are intended to explain the present application, and should not be construed as limiting the present application.

[0037] The method for detecting an object in a millimeter wave image according to an embodiment of the present application will be described below with reference to the accompanying drawings.

[0038] This application aims to solve one of the technical problems in the related art at least to a certain extent.

[0039] Therefore, the first purpose of this application is to propose a millimeter wave image target detection method, which solves the technical problem of detection and identification of dangerous objects in millime...

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Abstract

The invention provides a millimeter wave image target detection method, and relates to the technical field of computer vision, and the method comprises the steps: obtaining original millimeter wave image data; restoring three-dimensional space structure data of the millimeter wave image according to the data format of the original millimeter wave image data, and compressing the three-dimensional space structure data into two-dimensional plane data; carrying out noise reduction on the two-dimensional plane data, and carrying out standardization processing on the data after noise reduction; making a millimeter wave data set, analyzing characteristics of data in the millimeter wave data set, and selecting a deep learning model according to the characteristics of the data; training and testing the selected deep learning model by using the millimeter wave data set to obtain a test result of the deep learning model; and optimizing the deep learning model according to the test result and the evaluation index to obtain an optimal model. By adopting the scheme, the technical problems of positioning and identification of dangerous articles in the active millimeter wave image are solved, so that the human body security check efficiency in public places can be improved.

Description

technical field [0001] The present application relates to the technical field of computer vision, in particular to a method for detecting objects in millimeter wave images. Background technique [0002] Millimeter waves refer to electromagnetic waves with a wavelength of 1 to 10 mm. It has strong penetrating ability and can effectively detect dangerous objects hidden under human clothing. At the same time, millimeter waves have non-ionizing properties and will not cause harm to the human body. At present, most security checks in public places use traditional detection technologies: X-ray imaging systems and metal detectors. However, X-rays have great radiation to the human body and can only be used to check luggage items. The metal detector can only detect metal objects, and has no ability to inspect non-metallic objects. Due to these disadvantages of traditional security inspection technology, the application of millimeter waves in the field of human body security inspec...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/40G06N3/04G06N3/08G06T7/62
CPCG06T7/62G06N3/04G06N3/08G06T2207/20081G06V10/30G06V10/44G06V2201/07
Inventor 叶佐昌王燕宋照午
Owner TSINGHUA UNIV
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