Aerial image building detection and segmentation method and device based on Mask R-CNN

A building and aerial image technology, applied in image analysis, image enhancement, image data processing and other directions, can solve the problems of high cost, low detection and segmentation accuracy, achieve short training time, high detection and segmentation efficiency, and save training. effect of time

Active Publication Date: 2020-09-08
GUANGXI UNIV
View PDF8 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] For the scene of large-scale building segmentation, aiming at the problems of low detection and segmentation accuracy and high cost of buildings in aerial images, the object of the present invention is to provide

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
  • Aerial image building detection and segmentation method and device based on Mask R-CNN
  • Aerial image building detection and segmentation method and device based on Mask R-CNN
  • Aerial image building detection and segmentation method and device based on Mask R-CNN

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 2

[0064] The process of a method for detecting and segmenting buildings in aerial images based on Mask R-CNN has been introduced in detail above. This method can also be realized by a corresponding device. The structure and function of the device will be described in detail below.

[0065] An embodiment of the present invention provides a Mask R-CNN-based aerial image building segmentation device, which uses the aerial image building segmentation model trained in Embodiment 1.

[0066] The schematic diagram of the device is Figure 8 display, including:

[0067] The image input module is used for the user to input the aerial image of the building to be segmented into the network for segmenting;

[0068] The image deep feature extraction module is used to perform deep feature extraction on the input image data to obtain multi-scale feature maps;

[0069] The candidate area acquisition module is used to calculate the candidate area containing the building target from the feature...

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 the technical field of artificial intelligence detection. The invention relates to the field of aerial photography, in particular to an aerial photography building detection and segmentation method and device based on Mask R-CNN. The method comprises the steps that firstly, aerial images of urban buildings are acquired, the contours of building objects in the aerial imagesare marked, a training set and test set data are established, and a training data set is enhanced in a non-random covering data enhancement mode; constructing an aerial photo building detection and segmentation network; training the network by using the training data set, and performing test and performance evaluation on the trained segmentation model through test set data to obtain a final aerial photography graph building segmentation model; and applying the obtained model to the building aerial image needing to be processed by the user to obtain a final building aerial image segmentation image. According to the method, a deep learning method is used, the speed and the efficiency are improved, and the segmentation accuracy and the robustness of the model are improved by applying a transfer learning and non-random masking data enhancement method.

Description

technical field [0001] The invention relates to the technical field of artificial intelligence detection, in particular to a method and device for detecting and segmenting buildings in aerial images based on Mask R-CNN. Background technique [0002] With the rapid development of the national economy, the speed of urbanization in China is also getting faster and faster, and the number of various buildings in towns is increasing sharply. At the same time, the maturity of drone aerial photography technology has also made urban aerial images and videos Widely used in urban planning tasks. In the task of urban planning, 3D reconstruction in the task of building a digital city is the main trend of development, but there are various types of urban buildings and are constantly updated, such as urban villages and temporary factories on the outskirts of the city. Regularity and other issues, constructing a 3D digital model of a building is a task with a lot of work. In the 3D modeli...

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): G06T7/11
CPCG06T7/11G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30181
Inventor 许华杰张晨强苏国韶
Owner GUANGXI UNIV
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