Target detection method based on scene level and region suggestion self-attention module

A target detection and scene-level technology, applied in the field of image processing and computer vision, can solve the problem of low target detection accuracy and achieve the effect of improving accuracy

Active Publication Date: 2019-11-29
GUANGXI NORMAL UNIV
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AI Technical Summary

Problems solved by technology

[0009] What the present invention aims to solve is the problem that most current target detection researches only focus on the local information near the target region of

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  • Target detection method based on scene level and region suggestion self-attention module
  • Target detection method based on scene level and region suggestion self-attention module

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Embodiment Construction

[0035] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific examples.

[0036] see figure 1 , a target detection method based on the scene-level and region proposal self-attention module, which specifically includes the following steps:

[0037] Step 1. Build a target detection model with a depth-separable shared network, a scene-level-region proposal self-attention module, and a lightweight head network.

[0038] The present invention is based on a target detection model based on a depth-separable shared network, a scene-level-region suggestion self-attention module, and a lightweight head network. First, replace part of the convolutional residual modules in the backbone network with depth-separable shared volumes and networks, and construct a depth-separable shared convolution network to reduce computational complexity and improve computat...

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Abstract

The invention discloses a target detection method based on a scene-level and regional suggestion self-attention module, which combines various advanced network structures and concepts and considers the importance of scene information and semantic information to visual recognition. The method comprises the following steps: firstly, constructing a target detection model of a deep separable shared network, a scene level-region suggestion self-attention module and a lightweight head network; training the target detection model by using the training image to obtain a trained target detection model;and finally, sending the to-be-detected image into the trained target detection model to obtain position information and category information of the target in the image. The method is not limited tothe appearance characteristics of the target object in the image, but performs modeling characteristic extraction processing on the relationship information between the scene information and the object, and predicts the object in the image according to the structure, so that the detection accuracy can be greatly improved.

Description

technical field [0001] The invention relates to the technical fields of image processing and computer vision, in particular to a target detection method based on a scene-level and region suggestion self-attention module. Background technique [0002] As a classic topic in the field of image processing and computer vision, target detection is steadily improving from theoretical development to practical application. As the cornerstone of the computer vision field, it focuses on the detection of specific objects and requires the acquisition of category information and location information of the target at the same time. It differs from classification tasks in which objects are divided into individual categories, but instead gives an understanding of the object foreground and background, separates the object of interest from the background, and determines the description (category and location) of this object. In addition, in addition to being the basic element of classic compu...

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Application Information

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IPC IPC(8): G06K9/32G06K9/34G06K9/62G06N3/04
CPCG06V10/25G06V10/267G06N3/045G06F18/214
Inventor 李志欣权宇魏海洋张灿龙
Owner GUANGXI NORMAL UNIV
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