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Escalator safety monitoring method based on YOLOv3

A security monitoring and escalator technology, applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problems of complex models and inability to meet real-time monitoring requirements, to deepen the network depth, facilitate monitoring and review, and improve detection accuracy. Effect

Inactive Publication Date: 2019-03-19
NORTHEASTERN UNIV
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AI Technical Summary

Problems solved by technology

[0004] According to the above-mentioned technical problems in the traditional method for identifying pedestrian movements on escalators, the model is too complex to meet the requirements of real-time monitoring, and a YOLOv3-based escalator safety monitoring method is provided

Method used

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  • Escalator safety monitoring method based on YOLOv3
  • Escalator safety monitoring method based on YOLOv3
  • Escalator safety monitoring method based on YOLOv3

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Embodiment 1

[0028] Such as Figure 1-2 As shown, the present invention provides a kind of escalator safety monitoring method based on YOLOv3, comprises the steps:

[0029] Step 1: Intercept the pictures of passengers taking the elevator in the historical escalator monitoring video, perform data enhancement on the pictures to expand the data volume, mark the part of the characters in the pictures as the target area, and make Dataset 1 according to the PASCAL VOC data set format; among them, data enhancement The methods include rotation, horizontal flipping, shearing, resizing and adding image noise, etc.;

[0030] Preferably, the present invention adopts Labelimg software to go out the character part in the picture

[0031] Classify according to the posture of the person in the target area and make a data set 2 according to the PASCAL VOC data set format, where the posture of the person includes standing, falling, squatting and climbing the handrail;

[0032] Step 2: Use dataset 1 to tra...

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Abstract

The invention provides an escalator safety monitoring method based on YOLOv3, and the method comprises the following steps: 1, intercepting a picture with a passenger taking an escalator in a historical escalator monitoring video, marking a character part in the picture as a target region, and making a data set 1 according to a PASCAL VOC data set format; classifying according to the posture of the figure in the target area, and making a data set II according to a PASCAL VOC data set format; 2, training a YOLOv3 network model 1 by using the data set 1; Using the second data set to train a second YOLOv3 network model; 3, real-time safety monitoring is conducted on the escalator, and position coordinates of a target area are output through a YOLOv3 network model I; and the target area picture is the input of the YOLOv3 network model II to identify the postures of the passengers on the escalator. According to the technical scheme, the problems that a model is too complex and the real-timemonitoring requirement cannot be met in a traditional method for recognizing pedestrian actions on the escalator are solved.

Description

technical field [0001] The present invention relates to the technical field of machine vision, in particular to a YOLOv3-based escalator safety monitoring method. Background technique [0002] Traditional escalator safety detection methods mainly rely on the detection of a single physical quantity signal, which cannot identify passenger behavior and other information that occurs during normal operation of the escalator, and the accuracy and efficiency are relatively low. Traditional methods for action recognition are relatively complex and lack in real-time performance. With the rise of machine vision, the research on action recognition has become more in-depth, and an action recognition method based on the deep learning framework has been proposed. For action recognition, accuracy and speed are the two most important indicators, and high accuracy means that The real-time performance is poor, and two indicators should be considered comprehensively in practical applications....

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06F18/29
Inventor 李晶皎张震闫爱云王爱侠李贞妮
Owner NORTHEASTERN UNIV
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