CN111047575A - Unmanned aerial vehicle power line patrol image quality blind evaluation method - Google Patents
Unmanned aerial vehicle power line patrol image quality blind evaluation method Download PDFInfo
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- 238000007689 inspection Methods 0.000 claims abstract description 9
- 238000013507 mapping Methods 0.000 claims abstract description 5
- 238000007781 pre-processing Methods 0.000 claims abstract description 4
- 238000003708 edge detection Methods 0.000 claims description 3
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- G06—COMPUTING; CALCULATING OR COUNTING
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- G06T7/0002—Inspection of images, e.g. flaw detection
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Abstract
The invention relates to the technical field of image processing, in particular to a blind evaluation method for the quality of power line inspection images of an unmanned aerial vehicle, which comprises the following steps: s1, acquiring an electric power line patrol image transmitted back to the ground workstation by the unmanned aerial vehicle system, and preprocessing the electric power line patrol image; s2, layering the power line patrol image into a brightness image and a reflection image; s3, respectively calculating the quality scores of the brightness image and the reflection image; s4, respectively giving corresponding weights to the quality scores of the brightness image and the reflection image, and summing to obtain the comprehensive quality score of the power line patrol image; and S5, presetting a mapping relation between the comprehensive quality fraction and the quality grade, and obtaining the quality grade of the power line patrol image. The method can evaluate the quality of the unmanned aerial vehicle power line patrol image, and estimate the quality of the image according to brightness detection and ambiguity detection without a reference image.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a blind evaluation method for the quality of power line inspection images of an unmanned aerial vehicle.
Background
At present, the application of a multi-rotor unmanned aerial vehicle in fine routing inspection of a power transmission line is more and more extensive, the machine inspection picture is influenced by the experience of personnel, the data quality is uneven, the collected image often has the problems of inaccurate focusing, insufficient exposure or overexposure, unreasonable shooting angle, fuzzy pictures and the like, and the data collection quality is poor.
The quality of the image directly affects the subjective feeling of people and the information quantity acquisition, so the evaluation of the image quality is an important component of an unmanned aerial vehicle image system. In the unmanned aerial vehicle inspection image system, because reference images cannot be obtained for comparison, blind evaluation is often adopted for image evaluation.
Disclosure of Invention
The invention aims to provide a blind evaluation method for the quality of an unmanned aerial vehicle power line patrol image, which can evaluate the quality of the unmanned aerial vehicle power line patrol image without a reference image.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a blind evaluation method for the quality of power line inspection images of an unmanned aerial vehicle is characterized by comprising the following steps:
s1, acquiring an electric power line patrol image transmitted back to the ground workstation by the unmanned aerial vehicle system, and preprocessing the electric power line patrol image;
s2, layering the power line patrol image into a brightness image and a reflection image;
s3, respectively calculating the quality scores of the brightness image and the reflection image;
s4, respectively giving corresponding weights to the quality scores of the brightness image and the reflection image, and summing to obtain the comprehensive quality score of the power line patrol image;
and S5, presetting a mapping relation between the comprehensive quality fraction and the quality grade, and obtaining the quality grade of the power line patrol image.
Further, the step S2: and layering the power line patrol image by adopting a Retinex algorithm to obtain a brightness image and a reflection image.
Further, the calculating of the quality score of the luminance image in the step S3 includes: and obtaining a quality score model through a brightness threshold effect, and then calculating the quality score of the brightness image.
Further, the calculating of the quality score of the reflection image in the step S3 includes: and obtaining the edge width through a Canny edge detection algorithm, calculating the average edge width, and performing exponential operation on the average edge width to obtain the quality score of the reflection image.
The invention has the beneficial effects that: the method can evaluate the quality of the unmanned aerial vehicle power line patrol image, and estimate the quality of the image according to brightness detection and ambiguity detection without a reference image.
Drawings
Fig. 1 is a flowchart of a blind evaluation method for the quality of power line inspection images of an unmanned aerial vehicle in the embodiment.
Detailed Description
In order to better understand the present invention, the following embodiments are further described.
As shown in fig. 1, a blind evaluation method for the quality of power line inspection images of an unmanned aerial vehicle comprises the following steps:
s1, unmanned aerial vehicle power line patrol shooting image
And acquiring the electric power line patrol image transmitted back to the ground workstation by the unmanned aerial vehicle system, and preprocessing the electric power line patrol image.
S2, layering images
And layering the power line patrol image by adopting a Retinex algorithm to obtain a brightness image (incident component) and a reflection image (reflection component). According to Retinex theory, an image can be decomposed into an incident component (luminance image) and a reflected component (reflection image), wherein the incident component mainly represents visual perception information such as luminance and color of the image, and the reflected component mainly represents essential information such as texture and edge of the image.
S3, calculating the quality scores of the brightness image and the reflection image
And (3) carrying out brightness detection on the brightness image: calculating the mean value and the variance of the image on the gray level image, wherein when the brightness is abnormal, the mean value deviates from a mean value point, and the variance is smaller; by calculating the mean and variance of the gray scale map, it is possible to evaluate whether the image is over-exposed or under-exposed. And obtaining a quality score model LTEQ through a brightness threshold effect, and then calculating the brightness quality score of the brightness image.
And (3) carrying out ambiguity detection on the reflected image: and searching the edge width according to the edge walking direction by a Canny edge detection algorithm, obtaining the Canny edge width along gradient search, calculating the average edge width, and performing exponential operation on the average edge width to obtain the quality score of the ambiguity.
And S4, giving different weights to the brightness quality fraction of the brightness image and the fuzziness quality fraction of the reflection image, and summing to obtain the comprehensive quality fraction of the power line patrol image. And judging whether the image quality is good or not according to the obtained comprehensive quality score, wherein the obtained comprehensive quality score shows that the quality of the image is worse when the value is larger.
And S5, presetting a mapping relation between the comprehensive quality fraction and the quality grade of the image, substituting the obtained comprehensive quality fraction into the mapping relation, and obtaining the quality grade of the power line patrol image. And finally outputting a power line patrol image quality evaluation report.
The above description is only an application example of the present invention, and certainly, the present invention should not be limited by this application, and therefore, the present invention is still within the protection scope of the present invention by equivalent changes made in the claims of the present invention.
Claims (4)
1. A blind evaluation method for the quality of power line inspection images of an unmanned aerial vehicle is characterized by comprising the following steps:
s1, acquiring an electric power line patrol image transmitted back to the ground workstation by the unmanned aerial vehicle system, and preprocessing the electric power line patrol image;
s2, layering the power line patrol image into a brightness image and a reflection image;
s3, respectively calculating the quality scores of the brightness image and the reflection image;
s4, respectively giving corresponding weights to the quality scores of the brightness image and the reflection image, and summing to obtain the comprehensive quality score of the power line patrol image;
and S5, presetting a mapping relation between the comprehensive quality fraction and the quality grade, and obtaining the quality grade of the power line patrol image.
2. The unmanned aerial vehicle power line patrol image quality blind evaluation method according to claim 1, wherein the step S2: and layering the power line patrol image by adopting a Retinex algorithm to obtain a brightness image and a reflection image.
3. The unmanned aerial vehicle power patrol image quality blind evaluation method according to claim 1, wherein the calculating of the quality score of the luminance image in step S3 includes: and obtaining a quality score model through a brightness threshold effect, and then calculating the quality score of the brightness image.
4. The unmanned aerial vehicle power patrol image quality blind evaluation method according to claim 1 or 3, wherein the calculating of the quality score of the reflection image in the step S3 includes: and obtaining the edge width through a Canny edge detection algorithm, calculating the average edge width, and performing exponential operation on the average edge width to obtain the quality score of the reflection image.
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Cited By (2)
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| CN112093034A (en) * | 2020-09-22 | 2020-12-18 | 青海奥珞威信息科技有限公司 | Rotor magnetomotive unmanned aerial vehicle undercarriage retraction system |
| CN112215833A (en) * | 2020-10-22 | 2021-01-12 | 江苏云从曦和人工智能有限公司 | Image quality evaluation method, device and computer readable storage medium |
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