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Weak interaction object detection deep learning method and system

A deep learning and object detection technology, applied in the field of deep neural networks

Active Publication Date: 2018-09-14
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

But at present, most researchers conduct research on innovations in algorithmic network models, and rarely conduct research on how to improve data utilization (a large amount of unlabeled data) and improve the utilization of error samples.

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  • Weak interaction object detection deep learning method and system
  • Weak interaction object detection deep learning method and system
  • Weak interaction object detection deep learning method and system

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Embodiment Construction

[0044] The implementation of the present invention is described below through specific examples and in conjunction with the accompanying drawings, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific examples, and various modifications and changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention.

[0045] figure 1 It is a flow chart of the steps of a weakly interactive deep learning method for object detection in the present invention. Such as figure 1 As shown, a kind of weakly interactive object detection deep learning method of the present invention comprises:

[0046] Step S1, select some unlabeled data for manual labeling of object recognition, and combine with some public data...

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Abstract

The present invention discloses a weak interaction object detection deep learning method and system. The method comprises the steps of: the step S1, selecting some data without labels to perform manual marking of object identification and combine with a disclosed data set to an initial data set; the step S2, selecting a deep learning model, and employing the data with labels in the step S1 to perform training of the model; the step S3, employing the model obtained through training to perform feature extraction of the data without labels and the data with labels of the initial data set; the step S4, performing combination of the features, establishing a feature matrix, employing the data with the labels to perform label mapping for the data without labels, mapping the labels into the data without labels, and completing marking of the data without labels; the step S5, combining the data without labels and the data with labels to form a new label data training set according to a result inthe step S4; and the step S6, employing the new label data training set to repeatedly perform model training until the expression of the model achieves an expected effect.

Description

technical field [0001] The invention relates to the technical field of deep neural networks, in particular to a weakly interactive deep learning method for object detection and a system thereof. Background technique [0002] Image object classification and detection are two important basic problems in computer vision research. They are also the basis for other high-level visual tasks such as image segmentation, object tracking, and behavior analysis. They are very active research directions in the fields of computer vision, pattern recognition, and machine learning. . Object classification and detection are widely used in many fields, including face recognition, pedestrian detection, intelligent video analysis, pedestrian tracking, etc. in the security field, object recognition in traffic scenes, vehicle counting, retrograde detection, license plate detection and recognition in the traffic field, and Content-based image retrieval in the Internet field, automatic classificat...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06V2201/07G06F18/2155G06F18/214
Inventor 林倞陈浩钧王青江波
Owner SUN YAT SEN UNIV
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