Cigarette automatic detection method based on deep learning in monitoring scene

A deep learning and automatic detection technology, applied in the field of deep learning and computer vision, to achieve the effects of easy detection, improved contrast and details, and improved dynamic range

Active Publication Date: 2019-10-29
FUZHOU UNIVERSITY
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  • Claims
  • Application Information

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  • Cigarette automatic detection method based on deep learning in monitoring scene
  • Cigarette automatic detection method based on deep learning in monitoring scene
  • Cigarette automatic detection method based on deep learning in monitoring scene

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[0056] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0057] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0058] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinatio...

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Abstract

The invention relates to a cigarette automatic detection method based on deep learning in a monitoring scene, and the method comprises the steps: firstly carrying out the overturning, zooming and smoothing of a cigarette data set photographed and downloaded from a network, obtaining a bigger data set, training the data set based on a YOLOv3 deep learning network, and forming a template library; performing corresponding image enhancement processing on the to-be-detected image or video frame by using an image enhancement method; carrying out image segmentation on the large-size image, cigarettesor people with cigarettes in the image are separated out, and the time needed by detection is shortened; then pre-generating prediction boxes on the image to be detected, and comparing each prediction box with a pre-trained template library; and finally, selecting a prediction box higher than a preset threshold value from the detection confidence coefficients of all the prediction boxes, and determining the prediction box as a target object. After the whole image of the current frame is scanned, all detected targets are marked and displayed on the image, and cigarette detection is completed.The method can effectively improve the detection accuracy and shorten the detection time.

Description

technical field [0001] The invention relates to the fields of deep learning and computer vision, in particular to an automatic cigarette detection method based on deep learning in a monitoring scene. Background technique [0002] With the continuous improvement of modern people's living standards, concepts continue to improve. People pay more and more attention to the dangers of smoking. Recently, accidents caused by smoking have been well-known, such as: smoking on the high-speed rail caused the suspension of the high-speed rail, causing hundreds of passengers to be stranded; smoking at the gas station caused the gas station to catch fire, resulting in casualties and property losses; more What's more, forest fires were caused by smoking, and the great rivers and mountains of the motherland were destroyed. Therefore, smoking is strictly prohibited on high-speed railways and EMUs. In recent years, the punishment has been increased to the legal level; in public areas such as...

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Application Information

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IPC IPC(8): G06T7/00G06T7/10G06T7/136G06T5/00
CPCG06T5/002G06T7/0004G06T2207/10016G06T2207/20081G06T7/10G06T7/136
Inventor 柯逍黄旭
Owner FUZHOU UNIVERSITY
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