Full-convolutional-network aircraft detection method based on typical example mining

A fully convolutional network and detection method technology, applied in the field of full convolutional network aircraft detection, can solve problems such as low visual similarity, decreased recall rate of aircraft detection, and reduced training network performance, so as to achieve the effect of improving performance and

Active Publication Date: 2018-02-06
BEIHANG UNIV
View PDF3 Cites 15 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, all aircraft samples participating in network training usually have relatively low visual similarity due to differences in models and sizes. Therefore, if all traini

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
  • Full-convolutional-network aircraft detection method based on typical example mining
  • Full-convolutional-network aircraft detection method based on typical example mining
  • Full-convolutional-network aircraft detection method based on typical example mining

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0043] The present invention provides a full convolutional network aircraft detection method based on typical example mining. The method includes the following steps: obtaining training samples of remote sensing images; preprocessing the training samples to obtain the scale and scale of the expanded aircraft labeling truth box Aspect ratio; automatic mining of typical aircraft examples; construction of the correspondence between typical aircraft ...

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 provides a full-convolutional-network aircraft detection method based on typical example mining. The method comprises the following steps: acquiring a training sample of a remote sensingimage; preprocessing the training samples to obtain a scale and a length-width ratio of an extended aircraft marking true value frame; carrying out automatic mining of a typical aircraft example; constructing a correspondence relationship between the typical aircraft example and the aircraft marking true value frame; carrying out full convolutional network training based on the typical aircraft example mining; and realizing aircraft detection by using the full convolutional network. According to the invention, the full convolution neural network is introduced into remote sensing image aircraft detection; clustering is carried out on an aircraft sample based on aircraft configuration features; the typical aircraft example is mined in training data; and thus a candidate target extraction network for different typical aircraft examples is trained. Therefore, performances of the candidate region extraction network are improved and thus the recall ratio of the aircraft detection is increased; and a false alarm suppression effect is realized.

Description

technical field [0001] The present invention relates to the technical field of digital image processing, more specifically relate to a kind of fully convolutional network aircraft detection method based on typical example mining. Background technique [0002] The artificially designed low-level features in the current aircraft detection method for remote sensing images are easily disturbed by the complex background in remote sensing images, and the description ability is relatively insufficient; while training multiple classifiers for different scenarios often leads to exponential growth in computation and time overhead , it is difficult to meet the actual application requirements of aircraft detection tasks. In addition, in the traditional aircraft detection method based on convolutional neural network, during the training process of the network, all aircraft samples used for training are considered to have high visual similarity, and are used as a unified whole to train th...

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): G06T7/73G06K9/62
CPCG06T7/75G06T2207/10032G06T2207/20084G06T2207/20081G06T2207/20021G06F18/23213
Inventor 姜志国张浩鹏蔡博文谢凤英赵丹培史振威罗晓燕尹继豪
Owner BEIHANG 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