Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Terahertz dangerous article detection method based on depth learning

A technology of deep learning and detection method, which is applied in the field of terahertz dangerous goods detection based on deep learning, can solve the problem of high error rate, and achieve the effect of improving the detection rate, reducing the complexity and improving the calculation speed.

Inactive Publication Date: 2018-12-14
天和防务技术(北京)有限公司
View PDF19 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing detection methods have a high error rate for the detection of dangerous objects in terahertz images, and are not suitable for the detection of dangerous objects in terahertz images.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Terahertz dangerous article detection method based on depth learning
  • Terahertz dangerous article detection method based on depth learning
  • Terahertz dangerous article detection method based on depth learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0033] The deep learning-based terahertz dangerous goods detection method of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0034] Such as figure 1 As shown, the object to be detected in this embodiment is a human body, and the terahertz dangerous goods detection method based on deep learning includes the following steps:

[0035] 1. Build a sample image database of dangerous goods, classify dangerous goods, collect and mark the corresponding dangerous goods; at the same time, collect the sample library of dangerous goods according to the terahertz images obtained by terahertz detection of dangerous goods, add negative sample images, and then The image is processed into a grayscale image of the same size suitable for training and testing;

[0036] 2. Train the CNN neural network model, design and test the CNN network model, then adjust the parameters of learning rate, batsize and number of iterations, r...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a terahertz dangerous article detection method based on depth learning. The terahertz dangerous article detection method comprises steps that a dangerous article sample image database is established, and images are processed to be gray level images, which have the same size suitable for training and testing; a CNN neural network model is trained, and a final network model is generated and tested; dangerous article detection is carried out, and terahertz equipment is used to acquire terahertz images of to-be-detected objects are acquired, and the CNN neural network modelis used to detect the terahertz images of the acquired to-be-detected objects, and then detection results are acquired; and at the same time, the terahertz images of the acquired to-be-detected objects are added to the dangerous article sample image database. The CNN neural network is directly used for the training and the learning of the sample images, and complexity of selection of network parameters is reduced, and the obvious characteristics of the sample image data can be directly learned, and therefore problems of image classification and mode identification can be solved; working efficiency of security staff can be improved, workload of workers can be reduced, and the abovementioned method and the abovementioned device are suitable for security check of large flow of people.

Description

technical field [0001] The invention belongs to the field of dangerous goods detection methods, in particular to a terahertz dangerous goods detection method based on deep learning. Background technique [0002] The serious threat to public security by extremist organizations and extremists has become a serious problem faced by the security departments of various countries. The public security situation in our country is also not optimistic, and there have been many major public security incidents aimed at retaliating against society. Public safety is a major issue involving the vital interests of the people's lives, health, and property safety. All kinds of public security incidents have changed, showing many new characteristics such as suddenness, concealment, and complexity. Security inspection of personnel in important public places and public transportation stations is one of the most important means to prevent public safety incidents. The current general security ins...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G01V8/10G06F17/30G06K9/62G06N3/04G06N3/08
CPCG06N3/08G01V8/10G06N3/048G06F18/24
Inventor 张凯歌龚亚樵赵广州李世龙王虎
Owner 天和防务技术(北京)有限公司
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products