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A shoe sample and footprint key point detection method based on depth learning

A technology of deep learning and detection methods, applied in image data processing, instruments, electrical digital data processing, etc., can solve the problem of unable to solve the problem of target detail position, and achieve the effect of increasing the description of results evaluation indicators and reducing labor costs.

Active Publication Date: 2018-12-28
DALIAN EVERSPRY SCI & TECH
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

The outline of the detected target is better, especially in complex scenes with multiple targets, the semantic understanding is more prominent, but it also cannot solve the detailed position of the target

Method used

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  • A shoe sample and footprint key point detection method based on depth learning
  • A shoe sample and footprint key point detection method based on depth learning
  • A shoe sample and footprint key point detection method based on depth learning

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Embodiment Construction

[0065] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments: take this patent as an example to further describe and illustrate.

[0066] This embodiment provides a method for detecting key points of shoe samples and footprints based on deep learning, including the following steps:

[0067] S0. Define key points of shoe pattern / footprint:

[0068] Mark the four key points of the upper, lower, inner, and outer sides of the shoe sample data, such as figure 1 . Through the accumulation of time, a large number of shoe sample pictures and corresponding key point information are obtained; a large amount of shoe sample data is divided into a training library and a test library, and the images of the training library and the test library are normalized to 224*224*3 the size of;

[0069] Footprint data is divided into on-site footprint data and suspect footprint data. Use manual calibration to mark four...

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Abstract

The invention discloses a shoe sample and footprint key point detection method based on depth learning, which comprises the following steps: S1, obtaining shoe sample / footprint data climbing shoesample data pictures by using crawler technology, obtaining shoe sample pictures, marking key points by manual demarcation mode, and generating shoe sample data sets; 2, setting a network model; 3, calculating a loss function, and proposing a loss function based on that outline of the sole / footprint; S4, training the network model, adopting the transfer learning mode of partial network structureadjustment for training; 5, inputting the normalized image size into the train network model, and marking the coordinates of the output result on the original map. Using depth learning network to extract key point information makes it possible to mark footprints or footwear images by computer which greatly reduces the human cost.

Description

technical field [0001] The invention relates to a method for detecting key points based on deep learning, in particular to a method for detecting key points of shoe samples and footprints based on deep learning. Background technique [0002] Footprint information plays a pivotal role in the field of modern criminal investigation and investigation, and is one of the important physical evidence of on-site inspection. Through the trace characteristics reflected by the sole on the bearing body, not only the natural information such as the approximate height, weight, and age of the person can be initially analyzed and described, but also the walking posture, center of gravity and other characteristics of the person can be reflected through the wear information of the sole. The sole pattern information of the shoe sample can complement the incomplete footprint information, and through comparison, the incomplete pattern and worn areas of the footprint can be restored, which has pla...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/46G06K9/62G06T7/11G06T7/62G06F17/30
CPCG06T7/11G06T7/62G06T2207/20132G06V10/462G06F18/214
Inventor 孙晰锐于昕晔李岱熹崔均健赵晓蕊
Owner DALIAN EVERSPRY SCI & TECH
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