Three-dimensional target detection method, system and device based on RGB-D

A technology of three-dimensional target and detection method, which is applied in the field of target detection, can solve problems such as the inability to take into account the efficiency of 3D target detection accuracy, and achieve the effect of improving precision and accuracy, high precision and accuracy

Inactive Publication Date: 2020-09-29
COMMUNICATION UNIVERSITY OF CHINA
View PDF3 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In order to solve the above-mentioned problems in the prior art, that is, the problem that the prior art cannot take into account the efficiency and the accuracy

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Three-dimensional target detection method, system and device based on RGB-D
  • Three-dimensional target detection method, system and device based on RGB-D
  • Three-dimensional target detection method, system and device based on RGB-D

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

[0054] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0055] The present invention provides a three-dimensional target detection method based on RGB-D. This method uses a reverse mapping layer to map 2D image data into 3D space, and combines it with 3D point cloud data, so that the geometry of 3D point cloud can be fused. Features and ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention belongs to the technical field of target detection, particularly relates to a three-dimensional target detection method, system and device based on RGB-D, and aims to solve the problem that efficiency and 3D target detection accuracy cannot be both considered in the prior art. The method comprises the steps of performing feature extraction on a 2D image set of a to-be-detected target, and reversely mapping the 2D image set to a 3D space; performing voxel division on the 3D point cloud data of the to-be-detected target, and performing feature coding through a 3D convolutional neural network in combination with the mapping voxel of the 2D image; aggregating 2D image texture features and 3D point cloud data geometrical features; obtaining a target feature cluster set through a Hough voting network; and obtaining a target bounding box as a three-dimensional target detection result through the target regression and classification network. According to the method, the 2D imagedata is reversely mapped to the 3D space, the 3D point cloud geometrical characteristics and the 2D image texture characteristics are fused, the 3D target detection accuracy is improved, the pre-selected area is generated through adoption of the Hough voting method, and the 3D target detection efficiency is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of target detection, and in particular relates to an RGB-D-based three-dimensional target detection method, system and device. Background technique [0002] Environmental perception is not only an important issue in computer vision, but also affects the autonomous driving and intelligent robot industries. In the environmental perception problem, the perception technology for 2D image data and 3D point cloud data has achieved unprecedented development. Technologies such as the Faster-Rcnn neural network algorithm based on two-dimensional images and the PointRCNN based on three-dimensional point cloud data are widely used in the industrial field. [0003] There are many common methods in 3D object detection: the first is the projection method, which maps the 3D point cloud to the 2D image, and combines the feature extraction technology of the image. The typical method is the pyramid mapping method. When this...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/00G06V2201/07G06N3/045G06F18/23G06F18/241G06F18/253
Inventor 严明李中仝林茜茜金立标
Owner COMMUNICATION UNIVERSITY OF CHINA
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap