CN111666943A - Matching detection method and system for bolt and nut of power transmission line based on image recognition - Google Patents
Matching detection method and system for bolt and nut of power transmission line based on image recognition Download PDFInfo
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Abstract
The utility model relates to an image recognition field, specifically, relate to a transmission line bolt and nut's matching detection method and system based on image recognition, this method includes: arranging a label on the nut to be detected; acquiring an image containing a bolt and a nut to be detected; identifying a label in the image based on an image identification algorithm, and calculating to obtain a label pixel; identifying the nut in the image based on an image identification algorithm, and calculating to obtain a nut radius pixel; identifying the type of the bolt in the image based on an image identification algorithm, and determining the radius of the bolt according to the type of the bolt; calculating to obtain the nut radius according to the label pixel and the nut radius pixel; and judging whether the nut is matched with the bolt or not according to the radius of the nut and the radius of the bolt. The invention has the beneficial effects that: the image recognition algorithm is utilized to calculate the radiuses of the bolts and the nuts, the matching degree of the bolts and the nuts is detected, compared with manual judgment, the judgment accuracy and efficiency are greatly improved, and the use errors of the foundation bolts are reduced to the greatest extent.
Description
Technical Field
The disclosure relates to the field of image recognition, in particular to a matching detection method and system for a bolt and a nut of a power transmission line based on image recognition.
Background
In the field of intelligent monitoring of process quality of infrastructure construction sites at present, information-based intelligent support is weak, and matching detection of bolts and nuts of a power transmission line mainly depends on manual judgment. The following disadvantages exist in the artificial judgment: the judgment efficiency is low; the judgment accuracy is low.
Disclosure of Invention
In view of this, the present disclosure provides a method and a system for detecting matching of bolts and nuts of a power transmission line based on image recognition, which can detect matching of bolts and nuts on the power transmission line.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to one aspect of the disclosure, a matching detection method of a bolt and a nut of a power transmission line based on image recognition is provided, which includes:
arranging a label on the nut to be detected;
acquiring an image containing a bolt and a nut to be detected;
identifying a label in the image based on an image identification algorithm, and calculating to obtain a label pixel;
identifying the nut in the image based on an image identification algorithm, and calculating to obtain a nut radius pixel;
identifying the type of the bolt in the image based on an image identification algorithm, and determining the radius of the bolt according to the type of the bolt;
calculating to obtain the nut radius according to the label pixel and the nut radius pixel;
and judging whether the nut is matched with the bolt or not according to the radius of the nut and the radius of the bolt.
Preferably, the identifying the label in the image based on the image identification algorithm, and calculating the label pixel includes:
mapping RGB colors of the image to an HSV space range, carrying out binarization according to a set color threshold range, and extracting a label range;
acquiring the minimum circumscribed outline of the label through opencv in the range of the determined label;
and calculating to obtain the label pixel.
Preferably, the identifying the nut in the image based on the image identification algorithm, and calculating the nut radius pixel includes:
carrying out graying processing on the image, and acquiring and sequencing the image edges through opencv;
eliminating internal noise and eliminating connected regions;
extracting the maximum edge range, drawing a circumscribed circle of the edge area, and obtaining a nut radius pixel;
and calculating to obtain the nut radius pixel.
Preferably, the recognizing the bolt model in the image based on the image recognition algorithm, and determining the radius of the bolt according to the bolt model includes:
carrying out graying processing on the image and smoothing the image;
carrying out median filtering processing on the smoothed image;
carrying out binarization processing on the image subjected to median filtering processing;
recognizing characters in the image by using an OCR algorithm of template matching;
and determining the model of the bolt according to the recognized characters, thereby determining the radius of the bolt.
Preferably, the calculating the nut radius according to the label pixel and the nut radius pixel includes:
nut radius ═ label true length ═ nut radius pixel/label pixel.
According to an aspect of the present disclosure, a matching detection system for a bolt and a nut of a power transmission line based on image recognition is provided, which includes:
the image acquisition module is used for acquiring an image containing the bolt and the nut to be detected;
the first processing module is used for identifying a label in the image based on an image identification algorithm and calculating to obtain a label pixel;
the second processing module is used for identifying the nut in the image based on an image identification algorithm and calculating to obtain a nut radius pixel;
the third processing module is used for identifying the type of the bolt in the image based on an image identification algorithm and determining the radius of the bolt according to the type of the bolt;
the calculation module is used for calculating to obtain the nut radius according to the label pixel and the nut radius pixel;
and the detection module is used for judging whether the nut is matched with the bolt according to the radius of the nut and the radius of the bolt.
Preferably, the first processing module includes:
the label range extraction module is used for mapping the RGB color of the image to an HSV space range, carrying out binarization according to a set color threshold range and extracting a label range;
the label contour extraction module is used for acquiring the minimum circumscribed contour of the label through opencv in the range of the determined label;
and the label pixel calculation module is used for calculating to obtain a label pixel.
Preferably, the second processing module includes:
the image edge acquisition module is used for carrying out gray processing on the image, acquiring the image edges through opencv and sequencing the image edges;
the noise processing module is used for eliminating internal noise and eliminating a communication area;
and the nut radius pixel calculation module is used for extracting the maximum edge range and drawing a circumscribed circle of the edge area to obtain the nut radius pixels.
Preferably, the third processing module includes:
the preprocessing module is used for carrying out graying processing on the image and smoothing the image;
the median filtering processing module is used for carrying out median filtering processing on the image after the smoothing processing;
the binarization processing module is used for carrying out binarization processing on the image subjected to median filtering processing;
the character recognition module is used for recognizing characters in the image by utilizing an OCR algorithm matched with a template;
and the bolt radius calculation module is used for determining the model of the bolt according to the recognized characters so as to determine the radius of the bolt.
Preferably, the calculating module calculates the nut radius by:
nut radius ═ label true length ═ nut radius pixel/label pixel.
The invention has the beneficial effects that:
the image recognition algorithm is utilized to calculate the radiuses of the bolts and the nuts, the matching degree of the bolts and the nuts is detected, compared with manual judgment, the judgment accuracy and efficiency are greatly improved, and the use errors of the foundation bolts are reduced to the greatest extent.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
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The above and other objects, features and advantages of the present disclosure will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are merely some embodiments of the present disclosure, and other drawings may be derived from those drawings by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart illustrating a matching detection method for a bolt and a nut of a power transmission line based on image recognition according to an exemplary embodiment.
Fig. 2 is a flowchart illustrating step S3 in a method for detecting matching of a bolt and a nut of a power transmission line based on image recognition according to an exemplary embodiment.
Fig. 3 is a flowchart illustrating step S4 in a method for detecting matching of a bolt and a nut of a power transmission line based on image recognition according to an exemplary embodiment.
Fig. 4 is a flowchart illustrating step S5 in a method for detecting matching of a bolt and a nut of a power transmission line based on image recognition according to an exemplary embodiment.
Fig. 5 is a block diagram illustrating a matching detection system for a bolt and a nut of a power transmission line based on image recognition according to an exemplary embodiment.
Fig. 6 is a block diagram illustrating a first processing module in a matching detection system for a bolt and a nut of a power transmission line based on image recognition according to an exemplary embodiment.
Fig. 7 is a block diagram illustrating a second processing module in the matching detection system for the bolt and the nut of the power transmission line based on image recognition according to an exemplary embodiment.
Fig. 8 is a block diagram illustrating a third processing module in the matching detection system for the bolt and the nut of the power transmission line based on image recognition according to an exemplary embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one element from another. Thus, a first component discussed below may be termed a second component without departing from the teachings of the disclosed concept. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It is to be understood by those skilled in the art that the drawings are merely schematic representations of exemplary embodiments, and that the blocks or processes shown in the drawings are not necessarily required to practice the present disclosure and are, therefore, not intended to limit the scope of the present disclosure.
Fig. 1 is a flowchart illustrating a matching detection method for a bolt and a nut of a power transmission line based on image recognition according to an exemplary embodiment. As shown in fig. 1, the matching detection method for the bolt and the nut of the power transmission line based on the image recognition in the embodiment includes the following steps:
s1: and arranging the label on the nut to be detected.
In this embodiment, the nut radius is calculated by taking the tag as a reference, so the length of the tag is known. Under normal conditions, for convenient calculation, the label adopts a rectangular label. For identification purposes, the label is a different color than the nut, typically a vivid color.
S2: and acquiring an image containing the bolt and the nut to be detected.
And acquiring an image containing the bolt and the nut to be detected by photographing through an intelligent terminal with a photographing function. Preferably, the intelligent terminal has a remote transmission function and can realize image transmission.
S3: and identifying the label in the image based on an image identification algorithm, and calculating to obtain a label pixel.
As shown in fig. 2, the method specifically includes the following steps:
s31: mapping RGB colors of the image to an HSV space range, carrying out binarization according to a set color threshold range, and extracting a label range;
s32: acquiring the minimum circumscribed outline of the label through opencv in the range of the determined label;
s33: and calculating to obtain the label pixel.
Because the color of the label is obviously different from the color of the nut, the RGB color of the image is mapped to the HSV space range, binarization is carried out according to the set color threshold value, and the label range can be extracted quickly. It should be noted that the set color threshold is a color threshold of the label, so that interference caused by other contours on the image can be avoided.
After the minimum circumscribed outline of the label is obtained through opencv, the label pixel can be obtained through calculation according to the length of the outline.
S4: and identifying the nut in the image based on an image identification algorithm, and calculating to obtain the nut radius pixel.
As shown in fig. 3, the method specifically includes the following steps:
s41: carrying out graying processing on the image, and acquiring and sequencing the image edges through opencv;
s42: eliminating internal noise and eliminating connected regions;
s43: and extracting the maximum edge range, drawing a circumscribed circle of the edge area, and obtaining the nut radius pixel.
Through the steps, the interference of noise in the image can be eliminated, and the radius pixel of the nut is obtained through calculation by a method of drawing a circumscribed circle.
S5: and identifying the type of the bolt in the image based on an image identification algorithm, and determining the radius of the bolt according to the type of the bolt.
As shown in fig. 4, the method specifically includes the following steps:
s51: carrying out graying processing on the image and smoothing the image;
s52: carrying out median filtering processing on the smoothed image;
s53: carrying out binarization processing on the image subjected to median filtering processing;
s54: recognizing characters in the image by using an OCR algorithm of template matching;
s55: and determining the model of the bolt according to the recognized characters, thereby determining the radius of the bolt.
The purpose of the above steps is to recognize the characters on the bolt, and the model of the bolt can be determined according to the characters, so that the radius of the bolt is also determined.
For an image seriously interfered by noise, because many noise points are mapped into high-frequency components in a frequency domain, noise can be filtered by a filter through a low-frequency method and a method of taking neighborhood average value directly in a space domain, but in practice, in order to simplify an algorithm, the influence of the noise is weakened, and the method is called image smoothing processing.
The median filtering process is a nonlinear filtering technique, which is convenient because the statistical characteristics of the image are not needed in the actual calculation process. Because noise often appears in an isolated point form, the number of pixels corresponding to the points is small, and an image is composed of small blocks with more pixels and larger areas. Under certain conditions, the image detail blurring caused by a linear filter can be overcome, and the method is effective to filtering pulses and scanning noise of the image. More importantly, the median filtering process can effectively eliminate noise and effectively protect boundary information.
The OCR algorithm for template matching can quickly lock the characters in the image, and the algorithm itself is prior art, and thus is not described in detail.
S6: and calculating to obtain the nut radius according to the label pixel and the nut radius pixel.
The calculation formula is as follows: nut radius ═ label true length ═ nut radius pixel/label pixel.
And calculating to obtain the nut radius according to the nut radius pixel and the label pixel obtained by the step since the real length of the label is known.
S7: and judging whether the nut is matched with the bolt or not according to the radius of the nut and the radius of the bolt.
Since there is a specific comparison table between the nut radius and the screw radius, when determining whether the nut and the bolt are matched, data in the comparison table needs to be imported. Comparing the radius of the nut in the image with the radius of the standard nut corresponding to the bolt in the image, and if the radius of the nut in the image is the same as the radius of the standard nut corresponding to the bolt in the image, judging that the nut is matched with the bolt; if the two are different, the nut and the bolt are judged not to be matched.
The image recognition algorithm is utilized to calculate the radiuses of the bolts and the nuts, the matching degree of the bolts and the nuts is detected, compared with manual judgment, the judgment accuracy and efficiency are greatly improved, and the use errors of the foundation bolts are reduced to the greatest extent.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. When executed by the CPU, performs the functions defined by the above-described methods provided by the present disclosure. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the methods according to exemplary embodiments of the present disclosure, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the disclosed system that may be used to perform embodiments of the disclosed method. For details not disclosed in the embodiments of the system of the present disclosure, refer to the embodiments of the method of the present disclosure.
Fig. 5 is a diagram illustrating a matching detection system for a bolt and a nut of a power transmission line based on image recognition according to an exemplary embodiment. As shown in fig. 5, the present system includes: the image acquisition module is used for acquiring an image containing the bolt and the nut to be detected; the first processing module is used for identifying a label in the image based on an image identification algorithm and calculating to obtain a label pixel; the second processing module is used for identifying the nut in the image based on an image identification algorithm and calculating to obtain a nut radius pixel; the third processing module is used for identifying the type of the bolt in the image based on an image identification algorithm and determining the radius of the bolt according to the type of the bolt; the calculation module is used for calculating to obtain the nut radius according to the label pixel and the nut radius pixel; and the detection module is used for judging whether the nut is matched with the bolt according to the radius of the nut and the radius of the bolt.
In this embodiment, the image capturing module may be an intelligent terminal with a camera, a smart phone, or the like. Meanwhile, the intelligent terminal has a remote communication function and can transmit the acquired image to other remote modules.
As shown in fig. 6, the first processing module includes: the label range extraction module is used for mapping the RGB color of the image to an HSV space range, carrying out binarization according to a set color threshold range and extracting a label range; the label contour extraction module is used for acquiring the minimum circumscribed contour of the label through opencv in the range of the determined label; and the label pixel calculation module is used for calculating to obtain a label pixel.
As shown in fig. 7, the second processing module includes: the image edge acquisition module is used for carrying out gray processing on the image, acquiring the image edges through opencv and sequencing the image edges; the noise processing module is used for eliminating internal noise and eliminating a communication area; and the nut radius pixel calculation module is used for extracting the maximum edge range and drawing a circumscribed circle of the edge area to obtain the nut radius pixels.
As shown in fig. 8, the third processing module includes: the preprocessing module is used for carrying out graying processing on the image and smoothing the image; the median filtering processing module is used for carrying out median filtering processing on the image after the smoothing processing; the binarization processing module is used for carrying out binarization processing on the image subjected to median filtering processing; the character recognition module is used for recognizing characters in the image by utilizing an OCR algorithm matched with a template; and the bolt radius calculation module is used for determining the model of the bolt according to the recognized characters so as to determine the radius of the bolt.
The calculation module calculates the nut radius and comprises:
nut radius ═ label true length ═ nut radius pixel/label pixel.
Based on the fact that the implementation method and principle of each module have been disclosed in the method embodiment, further description is omitted.
Exemplary embodiments of the present disclosure are specifically illustrated and described above. It is to be understood that the present disclosure is not limited to the precise arrangements, instrumentalities, or instrumentalities described herein; on the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (10)
1. The matching detection method of the bolt and the nut of the power transmission line based on image recognition is characterized by comprising the following steps of:
arranging a label on the nut to be detected;
acquiring an image containing a bolt and a nut to be detected;
identifying a label in the image based on an image identification algorithm, and calculating to obtain a label pixel;
identifying the nut in the image based on an image identification algorithm, and calculating to obtain a nut radius pixel;
identifying the type of the bolt in the image based on an image identification algorithm, and determining the radius of the bolt according to the type of the bolt;
calculating to obtain the nut radius according to the label pixel and the nut radius pixel;
and judging whether the nut is matched with the bolt or not according to the radius of the nut and the radius of the bolt.
2. The matching detection method of the bolt and the nut of the power transmission line based on the image recognition as claimed in claim 1, wherein the step of recognizing the label in the image based on the image recognition algorithm and calculating the label pixel comprises the steps of:
mapping RGB colors of the image to an HSV space range, carrying out binarization according to a set color threshold range, and extracting a label range;
acquiring the minimum circumscribed outline of the label through opencv in the range of the determined label;
and calculating to obtain the label pixel.
3. The matching detection method of the bolt and the nut of the power transmission line based on the image recognition as claimed in claim 1, wherein the recognizing the nut in the image based on the image recognition algorithm and calculating to obtain the radius pixel of the nut comprises:
carrying out graying processing on the image, and acquiring and sequencing the image edges through opencv;
eliminating internal noise and eliminating connected regions;
and extracting the maximum edge range, drawing a circumscribed circle of the edge area, and obtaining the nut radius pixel.
4. The matching detection method of the image recognition-based transmission line bolt and nut as claimed in claim 1, wherein the recognizing of the bolt model in the image based on the image recognition algorithm and the determining of the bolt radius according to the bolt model comprises:
carrying out graying processing on the image and smoothing the image;
carrying out median filtering processing on the smoothed image;
carrying out binarization processing on the image subjected to median filtering processing;
recognizing characters in the image by using an OCR algorithm of template matching;
and determining the model of the bolt according to the recognized characters, thereby determining the radius of the bolt.
5. The image recognition-based matching detection method for the bolt and the nut of the power transmission line according to claim 1, wherein the step of calculating the nut radius according to the label pixel and the nut radius pixel comprises the following steps:
nut radius ═ label true length ═ nut radius pixel/label pixel.
6. Transmission line bolt and nut's matching detecting system based on image recognition, its characterized in that includes:
the image acquisition module is used for acquiring an image containing the bolt and the nut to be detected;
the first processing module is used for identifying a label in the image based on an image identification algorithm and calculating to obtain a label pixel;
the second processing module is used for identifying the nut in the image based on an image identification algorithm and calculating to obtain a nut radius pixel;
the third processing module is used for identifying the type of the bolt in the image based on an image identification algorithm and determining the radius of the bolt according to the type of the bolt;
the calculation module is used for calculating to obtain the nut radius according to the label pixel and the nut radius pixel;
and the detection module is used for judging whether the nut is matched with the bolt according to the radius of the nut and the radius of the bolt.
7. The image recognition-based matching detection system for the bolt and the nut of the power transmission line according to claim 6, wherein the first processing module comprises:
the label range extraction module is used for mapping the RGB color of the image to an HSV space range, carrying out binarization according to a set color threshold range and extracting a label range;
the label contour extraction module is used for acquiring the minimum circumscribed contour of the label through opencv in the range of the determined label;
and the label pixel calculation module is used for calculating to obtain a label pixel.
8. The image recognition-based matching detection system for the bolt and the nut of the power transmission line according to claim 6, wherein the second processing module comprises:
the image edge acquisition module is used for carrying out gray processing on the image, acquiring the image edges through opencv and sequencing the image edges;
the noise processing module is used for eliminating internal noise and eliminating a communication area;
and the nut radius pixel calculation module is used for extracting the maximum edge range and drawing a circumscribed circle of the edge area to obtain the nut radius pixels.
9. The image recognition-based matching detection system for the bolt and the nut of the power transmission line according to claim 6, wherein the third processing module comprises:
the preprocessing module is used for carrying out graying processing on the image and smoothing the image;
the median filtering processing module is used for carrying out median filtering processing on the image after the smoothing processing;
the binarization processing module is used for carrying out binarization processing on the image subjected to median filtering processing;
the character recognition module is used for recognizing characters in the image by utilizing an OCR algorithm matched with a template;
and the bolt radius calculation module is used for determining the model of the bolt according to the recognized characters so as to determine the radius of the bolt.
10. The image recognition-based matching detection system for the bolt and the nut of the power transmission line according to claim 6, wherein the calculation module for calculating the radius of the nut comprises:
nut radius ═ label true length ═ nut radius pixel/label pixel.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010311728.1A CN111666943A (en) | 2020-04-20 | 2020-04-20 | Matching detection method and system for bolt and nut of power transmission line based on image recognition |
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