YOLO v3-based detection method for key objects of transportation junction

A technology of transportation hubs and detection methods, which is applied in the field of image processing, can solve problems such as poor continuity and consistency, and achieve the goal of reducing the amount of model calculations, improving detection accuracy and speed, and taking into account network complexity and detection accuracy. Effect

Inactive Publication Date: 2019-11-19
JIANGXI UNIV OF SCI & TECH
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Problems solved by technology

However, since YOLO adopts the idea of ​​direct regression, it only identifies and processes each frame of image as ...

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  • YOLO v3-based detection method for key objects of transportation junction
  • YOLO v3-based detection method for key objects of transportation junction
  • YOLO v3-based detection method for key objects of transportation junction

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[0025] In order to make the purpose and technical solutions of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiments of the present invention. Apparently, the described embodiments are some, not all, embodiments of the present invention. Based on the described embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0026]Those skilled in the art can understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in commonly used dictionaries should be unde...

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Abstract

The invention discloses a YOLO v3-based detection method for key objects of a transportation junction. An algorithm is designed on the basis of the idea of direct regression to achieve multi-scale detection and multi-label classification. A Darknet-53 network based on ResNet is designed as a feature extractor based on the defects of current target detection technology, so that the detection precision and speed of YOLO technology architecture are improved. Meanwhile, the defect that the YOLO technology architecture is not good at detecting small objects is improved. The Darknet-53 network considers both the network complexity and the detection accuracy, and compared with a common target detection feature extraction network VGG-16, the model operand is reduced. The latest progress in the field of artificial intelligence is introduced into main target detection in the transportation hub, the detection precision and the detection speed are good, and meanwhile the method has the potential of being expanded and applied to other fields.

Description

technical field [0001] The present invention relates to the field of image processing, in particular to a method for detecting key objects in transportation hubs based on YOLO v3. Background technique [0002] With the rapid development of society, various high and new technologies continue to emerge, promoting the development of artificial intelligence. Among them, in the field of image processing, the technology of object recognition is developing rapidly. Image-based object detection technology is widely used in various industries. For example, image recognition technology is required in the fields of unmanned driving, unmanned supermarkets, remote sensing image recognition, biomedical detection, military and public security criminal investigation. Especially in the field of transportation, object recognition technology is gradually replacing the original technology for the detection and identification of pedestrians, motor vehicles, and non-motor vehicles. [0003] At ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/52G06F18/241
Inventor 杨杰康庄郭濠奇黄经纬何文玉张天露李家俊
Owner JIANGXI UNIV OF SCI & TECH
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