Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Unmanned aerial vehicle small target detection method based on motion features and deep learning features

A technology of small target detection and deep learning, applied in the field of small target detection of drones, can solve problems such as small size and difficult detection of drones

Active Publication Date: 2018-03-30
CHONGQING UNIV OF POSTS & TELECOMM
View PDF1 Cites 53 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

UAVs are smaller in size than pedestrians, airplanes, vehicles, etc. Especially in long-range imaging, the size of UAVs is very small, which makes vision-based UAV detection more difficult

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
  • Unmanned aerial vehicle small target detection method based on motion features and deep learning features
  • Unmanned aerial vehicle small target detection method based on motion features and deep learning features
  • Unmanned aerial vehicle small target detection method based on motion features and deep learning features

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0077] Hereinafter, the preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings.

[0078] In the present invention, the candidate target detection module based on motion characteristics performs video image stabilization on the original video, and then extracts the motion target region in the video through low-rank matrix analysis;

[0079] Candidate target detection module based on depth features, and extract candidate targets from video images through an improved region generation network model;

[0080] The improved candidate region generation network is to modify the network structure and the scale of the candidate region on the basis of the traditional region generation network, and replace the network layer of the output feature map;

[0081] The candidate region fusion module is to fuse the candidate regions obtained in steps S2 and S3;

[0082] The candidate target recognition module of the dual-channel deep neural n...

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 relates to an unmanned aerial vehicle (UAV) small target detection method based on motion features and deep learning features, which belongs to the technical field of image processing and computer vision. The method includes the following steps: processing an input video data set through a video image stabilization algorithm to make compensation for the motion of a camera; analyzingdetected moving candidate target regions in images; dividing the video data set into two parts, and carrying out training by using a training data set to get an improved candidate region generation network model; generating a candidate target for the video images of a test set through a candidate region generation network based on depth features obtained from training; fusing the candidate targetregions; carrying out training by using the training data set to get a deep neural network model based on dual channels, and obtaining an identification result by using the model; and applying a target tracking method based on multilayer depth features to the identification result in the previous step to get the final position of a UAV. A UAV in a video image can be accurately detected, and thus,support can be provided for the subsequent research in fields related to UAV intelligent monitoring.

Description

Technical field [0001] The invention belongs to the technical field of image processing and computer vision, and relates to a small target detection method for drones based on motion characteristics and deep learning characteristics. Background technique [0002] At present, with the sharp increase in the availability and maturity of commercial drones, the sales of drones have doubled, and drones flying in the public domain have become commonplace. UAVs not only appear in popular variety shows and romantic proposal ceremonies, they can also spray pesticides over farmland, replace workers in high-altitude cleaning operations, and are used for surveying and shooting, forest fire prevention, military reconnaissance, and so on. However, with the rapid development of drones, dangerous accidents caused by drones are also increasing, posing threats to public safety, privacy leakage, and military security. [0003] In recent years, detection drone technology can be roughly divided into ac...

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): G06T7/246G06N3/04
CPCG06T7/246G06T2207/10016G06T2207/20081G06N3/045
Inventor 高陈强杜莲王灿冯琦汤林汪澜
Owner CHONGQING UNIV OF POSTS & TELECOMM
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com