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A screening method for target detection training samples

A technology of training samples and screening methods, which is applied in the field of target detection training sample screening to achieve the effect of improving efficiency and accuracy

Active Publication Date: 2021-04-30
ZHEJIANG PECKERAI TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Each image sample is processed as follows:

Method used

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  • A screening method for target detection training samples
  • A screening method for target detection training samples
  • A screening method for target detection training samples

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Embodiment 1

[0023] Embodiment 1: In order to solve the above technical problems, this embodiment takes the model of the contraband detection scene as an example to illustrate the sample screening method, including the following steps:

[0024] 1. Use the models of different stages obtained during the training process to detect the targets in the training set data, and obtain the detection results of each image sample on the model M of different stages.

[0025] Among them, the training set is composed of manually labeled image samples. The number of image samples is huge, generally thousands or even hundreds of thousands. The acquisition of image samples is not limited to image acquisition, but also includes samples and / or a new sample obtained through an image fusion method disclosed in the prior art. In addition, there is no limitation on the types of images, which can be obtained by cameras, X-ray security inspection equipment, and terahertz imaging equipment. However, in general, gra...

Embodiment 2

[0037] Example 2: In Example 1 for M 1 -M 13 and M 14 After judging and filtering the forgotten samples to form the set C1, the recognition frame of the 1 -M 5 and M 6 -M 14 The obtained recognition frame is judged and screened to form a set C2 of forgotten samples, and the union of C1 and C2 is eliminated to obtain a new training set. Forgotten samples with different degrees of forgetting can be obtained by screening multiple times in different stages, and the specific number of times can be selected according to the actual effect.

[0038] The technical solution of the present invention can also be applied to target recognition and detection scenarios other than contraband detection in the embodiment, such as face recognition, license plate recognition, road recognition, unmanned driving, lesion detection and analysis in medical image CT inspection scenarios, etc. Various target detection scenarios.

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Abstract

The invention discloses a method for screening target detection training samples, which utilizes models obtained in different stages in the training process to detect objects in training set data, and obtains detection results of each image sample on a model M in different stages. Filter the detection results of the model at different stages for each image sample to obtain completely forgotten samples and partially forgotten samples. By using the model instead of manual analysis of a large number of noise samples in the target detection data set, it saves manpower and eliminates the subjective influence of artificially screening data, and improves the efficiency and accuracy of using deep learning methods to perform target detection tasks.

Description

technical field [0001] The invention belongs to the technical field of target detection, and in particular relates to a screening method for target detection training samples. Background technique [0002] In recent years, with the continuous development of artificial intelligence technology, deep learning technology has made breakthroughs in tasks such as classification, recognition, detection, segmentation, and tracking in the field of computer vision. Compared with traditional machine vision methods, deep convolutional neural networks can learn useful features from a large amount of data under the training of large data, which has the advantages of fast speed, high precision, and low cost. However, a large part of the reason why deep learning can achieve this superiority over traditional methods is because deep learning is based on a large amount of data, especially in the field of target detection, deep learning requires a large amount of effective data. In order to pro...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06K9/32G06N3/08
CPCG06N3/08G06V10/25G06V2201/07G06F18/24G06F18/214
Inventor 宋志龙
Owner ZHEJIANG PECKERAI TECH CO LTD
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