Light network construction method for embedded real-time visual target detection

A technology of target detection and real-time vision, applied in the direction of biological neural network model, neural architecture, character and pattern recognition, etc., can solve the problems of detection accuracy drop and processing speed drop, and achieve low processing speed, high accuracy, and accurate The effect of sex and real-time requirements

Active Publication Date: 2021-06-18
NANTONG UNIVERSITY
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Problems solved by technology

That is, high precision causes a significant drop in processing speed, and high speed causes a significant drop in detection accuracy.

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  • Light network construction method for embedded real-time visual target detection
  • Light network construction method for embedded real-time visual target detection
  • Light network construction method for embedded real-time visual target detection

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Embodiment Construction

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention, so that those skilled in the art can better understand the advantages and features of the present invention, so as to define the protection scope of the present invention more clearly. The embodiments described in the present invention are only a part of the embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without creative work For example, all belong to the protection scope of the present invention.

[0038] Such as figure 1 As shown, a light network construction method for embedded real-time visual target detection, including the following steps:

[0039] Step 101:) Construct Backbone-Tiny, a three-branch output backbone network module based on multi-scale feature fusion of channel attention mechanism; the sp...

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Abstract

The invention discloses a light network construction method for embedded real-time visual target detection. The light network construction method comprises the following steps: step 1) constructing a three-branch output backbone network module Backbone-Tiny based on channel attention mechanism multi-scale feature fusion; step 2), constructing a light pyramid feature fusion network module PAN-Tiny; step 3), constructing a light detection head network module Head-Tiny; and step 4) according to the sequence of the backbone network module Backbone-Tiny, the pyramid feature fusion network module PAN-Tiny and the detection head network module Head-Tiny, sequentially connecting the three local sub-network modules described in the step 1), the step 2) and the step 3) in series to form the whole target detection network. According to the method, the effectiveness of feature extraction of the backbone network module can be greatly improved, and meanwhile, the calculation amount of the feature pyramid fusion network module is remarkably reduced.

Description

technical field [0001] The present invention relates to image target detection and recognition technology, computer vision technology, in particular to a structure lightweight design method for real-time image target positioning and recognition (detection) deep learning model under the environment of embedded mobile terminal equipment. Background technique [0002] The so-called real-time image object detection model in the environment of embedded mobile terminal equipment refers to the development of a volume that can handle image object detection tasks exceeding 40 frames per second by using embedded technology in the environment of mobile equipment terminals without the upper PC. product neural network model. This technology provides important technical support for applications in industry, medicine, national defense, criminal investigation and other fields. [0003] At present, the common method is the target detection model of the YOLO series. Such as YOLOv3, YOLOv4, ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/045G06F18/253
Inventor 王晗冯文宇朱远璠郑君泰傅怀梁
Owner NANTONG UNIVERSITY
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