An Improved YOLOV3 Target Recognition Algorithm Embedded in SENet Structure

A target recognition and algorithm technology, applied in the field of deep learning, can solve the problems of misjudgment of parts in the background area, parts that cannot be correctly recognized, and low precision rate, etc., achieve high precision rate, improve detection effect, and reduce misidentification. Effect

Active Publication Date: 2019-01-15
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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Problems solved by technology

Due to the messy position of the parts, there are many incomplete parts in the pictures taken ( Image 6 Edge position), when using YOLOV3 network training, most of the incomplete parts cannot be correctly ident

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  • An Improved YOLOV3 Target Recognition Algorithm Embedded in SENet Structure
  • An Improved YOLOV3 Target Recognition Algorithm Embedded in SENet Structure
  • An Improved YOLOV3 Target Recognition Algorithm Embedded in SENet Structure

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Embodiment Construction

[0032] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is some embodiments of the present invention, but not all of them. Based on the implementation manners in the present invention, all other implementation manners obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention. Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the implementation manners in the present invention, all other implement...

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Abstract

The invention relates to the field of depth learning, in particular to an improved YOLOV3 target recognition algorithm embedded with a SENet structure, comprising the steps of S100: collecting characteristic information of an object to be recognized and making a data set; the feature information includes image information; S300, taking a part of the data set as a training set and the rest of the data set as a test set; Step S500:embedding SE structure in YOLOV3 algorithm to obtain SE-YOLOV3 algorithm; Step S600: training SE- YOLOV3 on the training set; Step S700: testing SE-YOLOVE3 performanceon the test set. An improved YOLOV3 target recognition algorithm embedded in a SENet structure of the present invention can accurately identify target parts when there are more incomplete parts disturbing in a sample picture, so as to obtain higher precision and recall ratio.

Description

technical field [0001] The invention relates to the field of deep learning, in particular to an improved YOLOV3 target recognition algorithm embedded in a SENet structure. Background technique [0002] Deep learning originated from the study of artificial neural networks, with the purpose of simulating the human brain to acquire and distinguish things. Its characteristic lies in the combination of low-level features to form an abstract high-level, so as to discover the distributed features of data. Convolutional Neural Network (CNN) is a deep learning method that has excellent performance for image processing. [0003] SENet is a convolutional neural network structure proposed by Hu Jie and his team in 2017, and won the ILSVRC2017 classification task champion. The structure consists of three parts: Squeeze, Excitaion, and Reweight, and explicitly builds the interdependence relationship between feature channels. Since it does not change the size of the feature map and does...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/44G06F18/23213G06F18/214
Inventor 刘学平李玙乾刘励
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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