IP Library Granted Patent US 11,145,082
Granted Patent B2
US 11,145,082 · App. 16/966,604 · Granted Oct 12, 2021

Method for measuring antenna downtilt angle based on deep instance segmentation network

Inventors: Yueting Wu (Jiangmen, CN); Yikui Zhai (Jiangmen, CN); Yu Zheng (Jiangmen, CN); Jihua Zhou (Jiangmen, CN); Tianlei Wang (Jiangmen, CN); Ying Xu (Jiangmen, CN); Junying Gan (Jiangmen, CN); Wenbo Deng (Jiangmen, CN); Qirui Ke (Jiangmen, CN)
Assignee: Wuyi University
G06T7/73G06N20/00G06T7/11G06T7/194H04W16/28G06T2207/10016G06T2207/10032G06T2207/20081
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Quick Facts
Patent No.
US 11,145,082
App. No.
16/966,604
Granted
Oct 12, 2021
Kind
B2
Abstract

A method for measuring an antenna downtilt angle based on a deep instance segmentation network is disclosed, including: shooting an omni-directional antenna video using a drone; and transmitting the antenna video to a server in real time, and the server measuring an antenna downtilt angle in real time using a deep learning algorithm, the deep learning algorithm includes: a feature extraction network module for acquiring an antenna feature image; an instance segmentation module for binary segmentation of an antenna image and the background to distinguish antenna image pixels from background pixels; an antenna candidate box module for identifying and detecting the antenna image, acquiring an antenna candidate box and determining an antenna calibration box therefrom; and an antenna downtilt angle measuring module for measuring an antenna downtilt angle.

Claims (182)

1. A method for measuring an antenna downtilt angle based on a deep instance segmentation network, comprising:

shooting an omni-directional antenna video using a drone; and

transmitting the antenna video to a server in real time, and the server measuring an antenna downtilt angle in real time using a deep learning algorithm,

wherein the deep learning algorithm comprises: a feature extraction network module for acquiring an antenna feature image; an instance segmentation module for binary segmentation of an antenna image and the background to distinguish antenna image pixels from background pixels; an antenna candidate box module for identifying and detecting the antenna image, acquiring an antenna candidate box and determining an antenna calibration box therefrom; and an antenna downtilt angle measuring module for measuring an antenna downtilt angle; and

the instance segmentation module comprises: directly mapping a region of interest to a feature map; segmenting a candidate region into k*k cells, determining, for each cell, four fixed coordinate positions of the center point of the cell, and calculating values of the four fixed coordinate positions using bilinear interpolation; and performing max-pooling and implementing back propagation.

2. The method for measuring an antenna downtilt angle based on a deep instance segmentation network according to claim 1 , wherein the shooting an omni-directional antenna video using a drone comprises: controlling the drone to fly around in a circle with an antenna as the origin and a radius of r meters from the antenna at a height of h meters from the antenna, and shooting a 360-degree omni-directional antenna video.

3. The method for measuring an antenna downtilt angle based on a deep instance segmentation network according to claim 2 , wherein the feature extraction network module comprises a residual network for solving gradient problems and network performance degradation problems and a feature pyramid network for solving multi-scale detection problems.

4. The method for measuring an antenna downtilt angle based on a deep instance segmentation network according to claim 1 , wherein the back propagation formula of the instance segmentation module is

L

x

i

=

r

j

[

d

(

i

,

i

*

(

r

j

)

)

<

1

]

(

1

-

Δ

h

)

(

1

-

Δ

w

)

L

y

ri

;

wherein x i represents a pixel point on the feature map before max-pooling; y rj represents the j th point in the r th candidate region after max-pooling; i*(r, j) represents coordinates of the pixel point before max-pooling corresponding to a pixel value of the point y rj ; x i *(r, f) represents coordinates of sampling points calculated during back propagation, d(⋅) denotes a distance between two points, and Δh and Δw denote differences of horizontal and vertical coordinates of x i and x i *(r, j).

5. The method for measuring an antenna downtilt angle based on a deep instance segmentation network according to claim 1 , wherein the antenna candidate box module is a candidate box selection module using a faster RCNN network structure.

6. The method for measuring an antenna downtilt angle based on a deep instance segmentation network according to claim 5 , wherein the antenna candidate box module's target loss function is

L

(

{

p

i

}

,

{

t

i

}

)

=

1

N

c

l

s

i

L

c

l

s

(

p

i

,

p

i

*

)

+

λ

1

N

r

e

g

i

p

i

*

L

r

e

g

(

t

i

,

t

i

*

)

;

wherein L cls is a classification loss function, and its expression is:

L cls ( p i ,p i *)=−log[ p i *p i +(1− p i *)(1− p i )];

L reg is a regression loss function, and its expression is: L reg (t i ,t i *)=R(t i −t i *); and

an R function is defined as

Smooth

L

1

(

x

)

=

{

0.5

x

2

if

x

<

1

x

-

0.5

otherwise

.

7. The method for measuring an antenna downtilt angle based on a deep instance segmentation network according to claim 1 , wherein the antenna downtilt angle measuring module obtains a region with a minimum relative proportion by comparing a relative proportion between the antenna calibration box and a region represented by the antenna image pixels.

8. The method for measuring an antenna downtilt angle based on a deep instance segmentation network according to claim 7 , wherein the antenna downtilt angle measuring module measures the antenna downtilt angle based on the following formula:

0

=

arctan

x

y

,

where x is the width of the calibration box, and y is the length of the calibration box.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 7, 2021
From: WU, YUETING; ZHAI, YIKUI; ZHENG, YU; ZHOU, JIHUA; WANG, TIANLEI; XU, YING; GAN, JUNYING; DENG, WENBO; KE, QIRUI
To: WUYI UNIVERSITY
Reel/Frame 056181/0288 →
Priority Claims (1)
CN 201811317915.X · Nov 6, 2018 · national
Continuity (1)
Related Publication 20210056722A1 · Feb 25, 2021