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An image detection method and device

A technology of image detection and image fragments, applied in the field of image recognition, can solve problems such as low detection efficiency

Inactive Publication Date: 2019-06-14
SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD
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  • Claims
  • Application Information

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Problems solved by technology

Although the cascaded convolutional neural network does not affect the efficiency of image detection, the detection efficiency is low when it is necessary to detect images with higher resolution

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Embodiment Construction

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0050] Embodiments of the present invention provide an image detection method and device, which can improve image detection efficiency. Each will be described in detail below.

[0051] see figure 1 , figure 1It is a schematic flowchart of an image detection method provided by an embodiment of the present invention. According to different needs, figure 1 Some of the steps in the flowcharts shown can be split into several steps. Such as figure...

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Abstract

The embodiment of the invention provides an image detection method and device. The method comprises the steps of obtaining a to-be-detected image; detecting the position of a human face in the to-be-detected image by using the first cascade convolutional neural network, wherein the first cascade convolutional neural network is a cascade convolutional neural network obtained by training according to a second cascade convolutional neural network; each of the first cascade convolutional neural network and the second cascade convolutional neural network is formed by cascading M convolutional neural networks, the complexity of the first cascade convolutional neural network is smaller than that of the second cascade convolutional neural network, and M is an integer greater than or equal to 3. Byimplementing the embodiment of the invention, the image detection efficiency can be improved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to an image detection method and device. Background technique [0002] In order to improve the accuracy of image detection without affecting the efficiency of image detection, the industry has introduced a cascaded convolutional neural network. In the process of using the cascaded convolutional neural network to detect images, all regions of the image must pass through the first convolutional neural network. Although the calculation of the first convolutional neural network is small, the number of regions that need to be judged is the largest. . The input image area of ​​the subsequent convolutional neural network comes from the area considered to be a face output by the previous convolutional neural network. Therefore, the number of areas that need to be judged is less than that of the previous convolutional neural network, but each area is judged. The amount of calcula...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
Inventor 吴伟吴涛杨龙张兆丰王孝宇田第鸿
Owner SHENZHEN INTELLIFUSION TECHNOLOGIES CO LTD