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Detection network model and object detection method

A network model and target detection technology, applied in the field of image processing, can solve the problem of low recognition ability and achieve effective detection

Active Publication Date: 2022-05-03
NAVINFO
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] Among them, the YOLO method can only detect the size of a fixed picture due to the introduction of a fully connected (Fully Connected) layer, and has a low recognition ability for small targets (smaller targets, such as people or objects in the distance).

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  • Detection network model and object detection method
  • Detection network model and object detection method
  • Detection network model and object detection method

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Embodiment Construction

[0050] 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 drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0051] The "superpixel" referred to in this article refers to a small area composed of a series of adjacent pixels with similar characteristics such as color, brightness, and texture. By superpixel segmentation, an original pixel-level image is divided into district-level images, one of which is a superpixel, and one superpixel is related to multi...

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Abstract

Embodiments of the present invention provide a detection network model and a target detection method. The detection network model of the present invention includes: a multi-scale fusion module, a superpixel classification module, a detection frame regression module, and N network basic modules connected in series; the N network basic modules respectively output image information with different scale features, and the The multi-scale fusion module is used to fuse the image information of the different scale features to generate fused data, the superpixel classification module classifies the fused data, and outputs the superpixel classification result, and the detection The frame regression module is used to perform detection frame regression processing on the fused data, and output a superpixel detection frame regression result. The invention can realize effective detection and identification of small targets.

Description

technical field [0001] Embodiments of the present invention relate to image processing technology, and in particular to a detection network model and a target detection method. Background technique [0002] In the fields of automatic driving, driving assistance and early warning, it needs to use various technical means, such as ultrasonic, radar, machine vision, infrared, etc., to obtain information about the surrounding environment of the vehicle, that is, to detect objects in the surrounding environment of the vehicle. Among them, target detection based on machine vision has low cost, small size, light weight, low power consumption, and wide visual range. [0003] The target detection method based on machine vision can specifically adopt the target detection method based on neural network. The target detection method based on neural network includes single-stage detection method and two-stage detection method. The two-stage detection method is mainly R-CNN (Regions with Co...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V10/764G06K9/62G06N3/04G06V10/80G06V10/774
CPCG06N3/04G06F18/241G06F18/251G06F18/214
Inventor 秦暕
Owner NAVINFO