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Subway foreign matter detection method, apparatus, and equipment, and subway shielded gate system

A foreign object detection and subway technology, applied in the field of image processing, can solve the problems of low efficiency and accuracy of foreign object detection and recognition in subways, and achieve the effect of low efficiency and accuracy

Active Publication Date: 2016-12-21
GUANGDONG UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In view of the above problems, the present invention provides a subway foreign object detection method, device, equipment and subway screen door system to solve the problem of low efficiency and accuracy in the detection and identification of subway foreign objects in the prior art

Method used

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  • Subway foreign matter detection method, apparatus, and equipment, and subway shielded gate system
  • Subway foreign matter detection method, apparatus, and equipment, and subway shielded gate system
  • Subway foreign matter detection method, apparatus, and equipment, and subway shielded gate system

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Embodiment 1

[0064] see figure 1 It is a schematic flow chart of a subway foreign object detection method provided in Embodiment 1 of the present invention. The method includes the following steps:

[0065] S11. Read the subway foreign object training sample, and acquire a two-dimensional color image in the training sample, perform preprocessing on the two-dimensional color image to extract its features, and obtain a processed two-dimensional color image, wherein the two-dimensional color image The three-dimensional color images include pictures containing subway foreign objects and pictures not containing subway foreign objects, and the capacity of training samples in the embodiment of the present invention is not limited.

[0066] S12. Obtain the convolutional neural network parameters obtained after training the processed two-dimensional color image through the classifier, and establish a trained convolutional neural network model;

[0067] Specifically, the softmax classifier is prefe...

Embodiment 2

[0081] Referring to embodiment one of the present invention and figure 1 The specific process of steps S11 to S16 described in , and see figure 2 Corresponding to the second embodiment of the present invention figure 1 The schematic flow chart of the specific preprocessing of the training samples in the step S11 shown, figure 1 Step S11 specifically includes:

[0082] S111. Read the subway foreign object training sample, and acquire the two-dimensional color image in the training sample;

[0083] S112. According to the structural requirements of the convolutional neural network, compress the two-dimensional color image into a two-dimensional color image with 64*64 pixels;

[0084] S113. Perform image segmentation on the two-dimensional color image with 64*64 pixels to obtain 8*8 image sub-regions;

[0085] Specifically, the image is divided into image sub-regions with a size of 8*8. The segmentation method is to divide the image into 8*8 image blocks from the upper left ...

Embodiment 3

[0134] Corresponding to the subway foreign object detection method disclosed in Embodiment 1 and Embodiment 2 of the present invention, Embodiment 3 of the present invention also provides a subway foreign object detection device, see Figure 5 It is a schematic structural diagram of a subway foreign object detection device provided in Embodiment 3 of the present invention, and the device specifically includes:

[0135] The first processing module 501 is used to read the subway foreign object training sample, and acquire the two-dimensional color image in the training sample, perform preprocessing on the two-dimensional color image to extract its features, and obtain the processed two-dimensional color image , wherein the two-dimensional color image includes a picture containing subway foreign objects and a picture not containing subway foreign objects;

[0136] Establishing module 502, for obtaining the convolutional neural network parameters obtained after training the proces...

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Abstract

The invention discloses a subway foreign matter detection method, apparatus, and equipment, and a subway shielded gate system. The subway foreign matter detection method comprises: reading a subway foreign matter training sample and extracting a feature of the sample; obtaining a convolutional neural network parameter and establishing a trained convolutional neural network model; obtaining a probability of including each kind of subway foreign matter in each two-dimensional color image; carrying out a statistic analysis to obtain a two-dimensional color image corresponding to a subway foreign matter kind with the highest occurrence probability; and detecting each two-dimensional color image including the subway foreign matter kind with the highest occurrence probability in the trained convolutional neural network model, thereby obtaining a detection result of a subway foreign matter causing warning by a subway early warning system. Therefore, a problem of low efficiency and precision of subway foreign matter detection and identification can be solved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method and device for detecting foreign objects in a subway based on a convolutional neural network, and a subway screen door system. Background technique [0002] In recent years, with the rapid development of my country's urban rail transit, the passenger flow continues to increase. How to ensure the safe and efficient travel of passengers has become the primary goal of subway operations. In order to ensure the safety of passengers, a screen door is generally set between the current subway platform and the train to prevent the dangerous situation of passengers when the passenger flow is crowded. However, there is a certain gap between the subway train and the screen door, if a passenger is clamped between the screen door and the train door at the instant the train is started, serious accidents will be caused. [0003] In order to eliminate the above-mentioned safety...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06N3/02
CPCG06N3/02G06T7/0002G06T2207/10024G06T2207/20081G06T2207/30232
Inventor 兰上炜李东曾宪贤梁健吕誉谢凯佳
Owner GUANGDONG UNIV OF TECH