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

Unmanned aerial vehicle single image rain removing method based on convolutional neural network

A convolutional neural network and a single image technology, applied in the field of computer vision, can solve problems that easily affect the visual navigation system and observation

Inactive Publication Date: 2021-04-20
HUNAN UNIV
View PDF6 Cites 3 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] UAVs are widely used in today's society, whether it is military unmanned reconnaissance drones or target drones, or civilian aerial photography, agriculture, plant protection, express transportation, disaster rescue, power inspection, disaster relief, film and television shooting, etc. , have greatly expanded the application field of UAVs, and both reconnaissance and self-navigation require the help of sufficiently clear images. However, UAVs are easily affected by harsh weather environments, such as rain and snow If the weather is different, the visual image with poor effect will be obtained, which not only affects the observation but also easily affects its own visual navigation system. Therefore, an effective UAV rain image removal algorithm is very important.

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 single image rain removing method based on convolutional neural network
  • Unmanned aerial vehicle single image rain removing method based on convolutional neural network
  • Unmanned aerial vehicle single image rain removing method based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0066] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0067] figure 1 It is a flowchart of a method for removing rain from a single image of a UAV based on a convolutional neural network provided by an embodiment of the present invention. Such as figure 1 Shown, the UAV single image based on convolutional neural network of the present invention removes rain

[0068] method, including the following steps:

[0069] S1, separating the rainy image into a low-frequency background image and a high-frequency rainline image through a guided filter;

[0070] S2, using a multi-scale CNN convolutional neural network to perform sub-channel feature extraction on low-frequency background images and high-frequency rainline images;

[0071] S3, reassign weight...

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 discloses an unmanned aerial vehicle single image rain removing method based on a convolutional neural network, and the method comprises the steps: firstly enabling a rain image to be separated into a low-frequency background image and a high-frequency rain line image through a guide filter; secondly, performing sub-channel feature extraction on the low-frequency background image and the high-frequency rain line image by using a multi-scale CNN convolutional neural network; re-allocating weights to the feature maps extracted by the multi-scale CNN convolutional neural network through a channel domain attention mechanism; then, splicing the feature maps of the plurality of channels through a full connection layer, and constructing a mapping relationship between the rain image and the rain-removed image; and finally, respectively outputting high-frequency and low-frequency rain-removed images, and fusing the high-frequency and low-frequency rain-removed images to output a complete rain-removed image. The method has the characteristic of efficiently removing the rain line on the rainy day image shot in the flight process of the unmanned aerial vehicle.

Description

technical field [0001] The present invention relates to the technical field of computer vision, and more specifically, to a method for removing rain from a single image of a UAV based on a convolutional neural network. Background technique [0002] UAVs are widely used in today's society, whether it is military unmanned reconnaissance drones or target drones, or civilian aerial photography, agriculture, plant protection, express transportation, disaster rescue, power inspection, disaster relief, film and television shooting, etc. , have greatly expanded the application field of UAVs, and both reconnaissance and self-navigation require the help of sufficiently clear images. However, UAVs are easily affected by harsh weather environments, such as rain and snow If there is no weather, the visual image with poor effect will be obtained, which not only affects the observation but also easily affects its own visual navigation system. Therefore, an effective UAV rain image removal ...

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
Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06N3/04G06N3/08
Inventor 孔镜如周四望
Owner HUNAN UNIV
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