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Target detection positioning optimization method based on unsupervised domain adaptation

A target detection and positioning optimization technology, which is applied in the directions of instruments, calculations, character and pattern recognition, etc., can solve the problems of reducing the robustness and effectiveness of target detection, insufficient object positioning capabilities, etc., and achieve good target detection results

Active Publication Date: 2019-07-05
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

The existing domain-adaptive target detection network, its domain-adaptive structure comes from the domain-adaptive classification network; compared with the classification task, the task of target detection requires the prediction of the specific location of the object, which requires higher precision of features, so the current Domain-adapted object detection networks underperform on object localization capabilities, reducing the robustness and effectiveness of object detection

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  • Target detection positioning optimization method based on unsupervised domain adaptation
  • Target detection positioning optimization method based on unsupervised domain adaptation
  • Target detection positioning optimization method based on unsupervised domain adaptation

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Embodiment Construction

[0066] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific illustrations.

[0067] see figure 1 A target detection and positioning optimization method based on unsupervised domain adaptation provided in this embodiment includes the following steps:

[0068] 1 Data preprocessing

[0069] For the two data sets used for training, the existing labeled data set is defined as the source domain, and the unlabeled data set is defined as the target domain; part of the data in the target domain is divided into a verification set, other target domain data and The source domain data is used as the training set, and the images and labels are converted into the format required for training the deep convolutional network through preprocessing.

[0070] Step 1.1: Scale the image in the data set to a pixel size of m×n in length and w...

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Abstract

The invention discloses a target detection positioning optimization method based on unsupervised domain adaptation, which aims to solve the technical problem that an existing domain adaptation targetdetection method is insufficient in positioning capability for the position of an object under the condition that the object migrates from a labeled data set to an unlabeled data set. The method comprises the steps of (1) processing the data, (2) constructing a model, (3) defining a loss function, (4) training the model and (5) verifying the model. The present invention provides a new feature extraction network model which is suitable for the unlabeled data set, the positioning is more optimized, and the representation effect of the object positioning is good.

Description

technical field [0001] The invention relates to the technical field of computer image processing, and mainly relates to an optimization method for target detection and positioning based on unsupervised domain adaptation. Background technique [0002] Object detection and recognition is one of the important topics in the field of computer vision computing. With the development of human science and technology, the important technology of target detection is constantly being fully utilized. People apply it to various scenarios to achieve various expected goals, such as site security, safety detection, traffic control, video surveillance, etc. . [0003] In recent years, with the rapid development of deep learning, deep convolutional neural networks have made further breakthroughs in target detection and recognition technology. However, labeling for target detection data sets is very cumbersome and time-consuming. Therefore, people try to use the existing labeled data set to g...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06K9/46
CPCG06V40/63G06V10/454G06F18/24G06F18/214Y02T10/40
Inventor 徐雪妙余宇山胡枭玮
Owner SOUTH CHINA UNIV OF TECH
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