CN111488820B - Intelligent cable tunnel engineering inspection method and system based on light and shadow separation - Google Patents
Intelligent cable tunnel engineering inspection method and system based on light and shadow separation Download PDFInfo
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Abstract
The invention discloses an intelligent inspection method for cable tunnel engineering based on light and shadow separation, which comprises the steps of performing shadow separation and removal on an acquired visible light image; performing feature recognition according to the image information with the shadow information removed to obtain names of all devices in the image and device state information; judging whether the association relation of two kinds of devices in the image meets M ij =0, if yes, deleting detection recognition results of the two types of equipment; and issuing and displaying the inspection alarm information of the cable tunnel engineering. The invention also provides a cable tunnel engineering intelligent inspection system based on light and shadow separation, which comprises an image sensing module, a shadow detection module, a joint detection module and a display alarm module, and is used for publishing and displaying cable tunnel engineering inspection alarm information on the result obtained by the joint detection module. By adopting the technical scheme, the intelligent inspection accuracy of the cable tunnel engineering can be effectively improved.
Description
Technical Field
The invention relates to an intelligent inspection method and system for cable tunnel engineering based on light and shadow separation, and belongs to the technical field of electric power operation inspection.
Background
In recent years, the construction of urban underground distribution networks is becoming more and more sophisticated. The construction and maintenance of the cable tunnel are increasingly important because of the problems of water seepage, dampness, cable aging, equipment corrosion and damage and the like caused by the natural geographical environment of the cable tunnel. At present, the maintenance of cables and matched equipment in a tunnel is mainly finished manually, and a great deal of dense smoke, harmful gas, high-voltage cable leakage, cable tunnel collapse and other problems in the tunnel are likely to threaten the life safety of staff. With the development of robotics and image recognition technologies, some expert scholars propose to detect the internal condition of a cable tunnel by using a routing inspection robot, and gradually become an effective solution, for example, the implementation and application of a routing inspection robot in a cable tunnel disclosed in the journal literature of China (pages 73-74 of 17 th 2019) and the cable tunnel rust recognition algorithm based on a transfer learning convolutional neural network disclosed in the journal literature of China (pages 104-110 of 52 th 2019). However, the cable tunnel is a low-light environment lacking natural illumination, and part of the inspection robots use illumination equipment such as incandescent lamps or LEDs to supplement light, so that shadows generated by the point light sources in the claustrophobic environment seriously affect the quality of image acquisition and restrict the precision of inspection analysis. At present, a method for separating and removing shadows of an acquired video image is available, for example, a shadow detection and removal algorithm for combining colors and gradients in a complex environment is disclosed in Chinese patent document CN107886502A, and an idea based on combining colors and gradients is added into a shadow detection algorithm to be fused with a Gaussian mixture background model, so that the problems of target adhesion, shape change, target loss, false target occurrence and the like caused by shadows in the complex environment are solved after shadow detection, removal and target foreground reconstruction are carried out. Also, for example, chinese patent document CN106296666a discloses a color image shadow removing method and application, which can remove shadows existing in a color image, and is applied as a preprocessing step to various machine vision fields. In addition, besides the influence of shadows on image recognition, recognition accuracy is reduced, and the problem of high false alarm rate exists in the intelligent inspection of the cable tunnel engineering at present.
Disclosure of Invention
Therefore, the invention aims to provide the intelligent inspection method and the intelligent inspection system for the cable tunnel engineering based on light and shadow separation, which realize the improvement of the accuracy and the suppression of the false alarm rate of intelligent inspection and provide technical support for the integral improvement of the intelligent inspection capability of the cable tunnel engineering.
In order to achieve the above purpose, the intelligent inspection method for the cable tunnel engineering based on light and shadow separation comprises the following steps:
(1) Shadow separation and removal are carried out on the collected visible light images;
(2) Performing feature recognition according to the image information with the shadow information removed to obtain names of all devices in the image and device state information;
(3) Obtaining any device Name l Belongs to category Ω l E {1,2, …, S }, retrieving a pre-stored data matrix M e R S×S The method comprises the steps of carrying out a first treatment on the surface of the Judging whether the association relation of two kinds of devices in the image meets M ij =0, if yes, deleting detection recognition results of the two types of equipment;
wherein, the data matrix M epsilon R S×S Storing the association relation of equipment in cable tunnel engineering, wherein S represents the class number of the equipment in the cable tunnel engineering, and M is the number if the equipment of the ith class and the equipment of the jth class can occur simultaneously in the same scene ij =1; otherwise, M ij ＝0；
(4) And (3) based on the result obtained in the step (3), issuing and displaying the cable tunnel engineering inspection alarm information.
In the step 1, the separating and removing of the shadow portion includes the steps of:
(11) Image I of visible light RGB ＝(L R ,L G ,L B ) Conversion from RGB color space to intrinsic color space to form intrinsic image I Int ＝(I 1 ,I 2 ,I 3 )，
I 1 ＝L R +L G -β 1 L B
I 2 ＝L R -β 2 L G +L B
I 3 ＝-β 3 L R +L G +L B
Wherein (I) 1 ,I 2 ,I 3 ) Representing eigenvalues in the eigenvalue color space; (L) R ,L G ,L B ) Representing gray values in an RGB color space; beta i I=1, 2,3, which is the conversion coefficient of RGB color space to intrinsic color space;
(12) By screening I 3 Not less than k as visible light image I RGB Is realized for visible light image I by shadow information mode RGB Shadow information I is separated from the cable tunnel engineering inspection image by detecting and separating the shadow information of the cable tunnel engineering inspection image RGB-sha And intrinsic image information I RGB-cor The method comprises the steps of carrying out a first treatment on the surface of the Wherein I is RGB ＝I RGB-cor +I RGB-sha K is a preset value.
(β 1 ,β 2 ,β 3 ) = (-0.7261,0.3816,0.3316) is the empirically chosen conversion coefficient of the RGB color space into the intrinsic color space.
k＝65。
The invention also provides an intelligent inspection system for the cable tunnel engineering based on light and shadow separation, which comprises the following components:
the image sensing module is used for acquiring visible light images in the cable tunnel engineering inspection;
the shadow detection module is used for carrying out shadow separation and removal on the visible light image acquired by the image sensing module;
the joint detection module comprises a device detection and identification module, an expert priori knowledge module and a semantic logic identification module; the equipment detection and identification module is used for detecting and identifying all equipment names and equipment state information based on the evidence image information; the expert priori knowledge module is used for using the data matrix M epsilon R S×S Pre-storing the association relation of the devices in the cable tunnel engineering in a form, wherein S is the number of device categories in the cable tunnel engineering, and M is the number of the devices in the cable tunnel engineering if the ith device and the jth device can simultaneously appear in the same scene ij =1, otherwise, M ij =0; the semantic logic recognition module is used for acquiring any device Name detected and recognized by the device detection recognition module l Class Ω to which it belongs l E {1,2, …, S }, retrieving a pre-stored data matrix M e R S ×S The method comprises the steps of carrying out a first treatment on the surface of the Judging whether the association relation of two kinds of devices in the image meets M ij =0, if yes, deleting detection recognition results of the two types of equipment;
and the display alarm module is used for publishing and displaying the cable tunnel engineering inspection alarm information for the result obtained by the joint detection module.
Shadow detection module for detecting visible light image I RGB ＝(L R ,L G ,L B ) Conversion from RGB color space to intrinsic color space, shapeCost characterization image I Int ＝(I 1 ,I 2 ,I 3 )，
I 1 ＝L R +L G -β 1 L B
I 2 ＝L R -β 2 L G +L B
I 3 ＝-β 3 L R +L G +L B
Wherein (I) 1 ,I 2 ,I 3 ) Representing eigenvalues in the eigenvalue color space; (L) R ,L G ,L B ) Representing gray values in an RGB color space; beta i I=1, 2,3, which is the conversion coefficient of RGB color space to intrinsic color space;
for screening I 3 Not less than k as visible light image I RGB Is realized for visible light image I by shadow information mode RGB Shadow information I is separated from the cable tunnel engineering inspection image by detecting and separating the shadow information of the cable tunnel engineering inspection image RGB-sha And intrinsic image information I RGB-cor The method comprises the steps of carrying out a first treatment on the surface of the Wherein I is RGB ＝I RGB-cor +I RGB-sha K is a preset value.
Compared with the prior art, the invention has the following beneficial effects:
by adopting the technical scheme, the intelligent inspection method and the intelligent inspection system for the cable tunnel project based on light and shadow separation can effectively improve the accuracy of intelligent inspection of the cable tunnel project by separating and removing the shadow part in the original image and identifying the rest part; and by utilizing expert priori knowledge to verify the semantic relevance of the target detection and identification result, the false alarm rate of intelligent inspection of the cable tunnel engineering can be effectively inhibited.
Drawings
Fig. 1 is a flow chart of the intelligent inspection method of the cable tunnel engineering based on light and shadow separation.
Fig. 2 is a block diagram of a cable tunnel engineering intelligent inspection system based on light and shadow separation.
Detailed Description
The invention is described in further detail below with reference to the drawings and the detailed description.
The embodiment of the invention discloses an intelligent inspection method and system for cable tunnel engineering based on light and shadow separation. In order to eliminate interference of light and shadow overlapping in images acquired in a tunnel on algorithm accuracy, the method comprises the steps of firstly separating a device body and shadows in cable tunnel engineering, and further utilizing semantic relevance of target detection and identification results, and realizing accuracy improvement and false alarm rate suppression of intelligent inspection by connecting the two modules in series. Taking an XXX transmission line 110KV cable tunnel as an example, the present invention is implemented by the following steps S1 to S6:
(S1) the visible light image I of the target equipment acquired by the image sensing module C1 is obtained by the following formula RGB ＝(L R ,L G ,L B ) Conversion from RGB color space to intrinsic color space to form its intrinsic image I Int ＝(I 1 ,I 2 ,I 3 )。
I 1 ＝L R +L G -β 1 L B
I 2 ＝L R -β 2 L G +L B
I 3 ＝-β 3 L R +L G +L B
Wherein (I) 1 ,I 2 ,I 3 ) Representing eigenvalues in the eigenvalue color space; (L) R ,L G ,L B ) Representing gray values in an RGB color space; (beta) 1 ,β 2 ,β 3 ) = (-0.7261,0.3816,0.3316) is the empirically pre-selected conversion coefficient of the RGB color space into the intrinsic color space.
(S2) by screening I using shadow detection module C2 3 65 or more as visible light image I RGB The shadow information is detected and separated, and then the shadow information I is removed from the cable tunnel engineering inspection image RGB-sha And intrinsic image information I RGB-cor The method comprises the steps of carrying out a first treatment on the surface of the Wherein I is RGB ＝I RGB-cor +I RGB-sha 。
(S3) inspection by the device inspection recognition module C31Measuring intrinsic image information I RGB-cor Detecting a device Name identifying a first device l = 'cable joint', position information (x l max ,y l max ,x l min ,y l min ) = (125,48,107,39), device Status l = 'breakage'.
(S4) using expert a priori knowledge module C32 to form data matrix M ε R S×S Storing the association relation of equipment in cable tunnel engineering; s represents the number of equipment categories in cable tunnel engineering; if the ith device and the jth device possibly occur simultaneously in the same scene, M ij =1; otherwise, M ij ＝0。
For example, as shown in the following table:
(S5) determining any one of the device names Name by using the semantic logic recognition module C33 l Belongs to category Ω l E {1,2, …, S }, further according to the data matrix M e R stored in step S4 S×S Judging the intrinsic image information I RGB-cor Whether the association relation of two kinds of devices meets M ij =0; if M is present ij And (4) deleting detection and identification results of the two types of devices. For example, it is determined that the 'cable' belongs to the first type of device, and the 'switch cabinet' appears in the same scene as the 'cable' through the detection and identification of the device detection and identification module C31, and the 'switch cabinet' belongs to the second type of device, and the association relationship between the first type of device and the second type of device can be obtained through the foregoing table to satisfy M ij And if the device is=0, indicating that errors exist in the identification, and deleting detection and identification results of the two types of devices.
And (S6) utilizing the display alarm module C4 to issue and display the cable tunnel engineering inspection alarm information based on the result obtained in the step S5. For example, "cable joint" broken in 110KV cable tunnel of XXX transmission line "needs to be repaired as soon as possible. And meanwhile, carrying out visual display in an original image in the form of a frame according to the equipment coordinate information output in the step S3.
Fig. 2 is a schematic diagram of an intelligent inspection system for cable tunnel engineering based on light and shadow separation in an embodiment of the invention. Schematic diagram the system comprises: the system comprises an image sensing module, a shadow detection module, a joint detection module and a display alarm module. Taking an XXX transmission line 110KV cable tunnel as an example, the specific module functions are detailed as follows:
the image sensing module C1 supports the acquisition function of visible light image data of equipment such as cables, cable joints, switch cabinets and the like in the 110KV cable tunnel engineering of the power transmission line through a visible light sensor to which the image sensing module C belongs.
The shadow detection module C2 supports detection and separation of shadow information in the cable tunnel engineering inspection image acquired by the image sensing module C1, and further removes the shadow information from the cable tunnel engineering inspection image so as to extract intrinsic image information of the cable tunnel engineering inspection image.
The combined detection module C3 supports the function implementation of the equipment detection and identification module C31, the expert priori knowledge module C32 and the semantic logic identification module C33; the equipment detection and identification module C31 is used for detecting and identifying the position and working condition of equipment in the cable tunnel engineering inspection image based on the intrinsic image information, and outputting equipment names, equipment states and equipment coordinates in an XML file format; the expert priori knowledge module C32 is used for storing the association relation of equipment in the cable tunnel engineering; the semantic logic identification module C33 judges and filters false alarm events based on the association relation of the equipment in the cable tunnel engineering stored by the expert priori knowledge module C32.
The display alarm module C4 issues and displays the inspection alarm information of the cable tunnel engineering, namely the device name and the device state in the 110KV cable tunnel of the XXX power transmission line according to the result obtained by the joint detection module C3, and the inspection alarm information needs to be repaired as soon as possible. For example, "cable joint" broken in 110KV cable tunnel of XXX transmission line "needs to be repaired as soon as possible. And meanwhile, carrying out visual display in an original image in a frame form according to the equipment coordinate information output by the joint detection module C3.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (4)
1. The intelligent inspection method for the cable tunnel engineering based on the light and shadow separation is characterized by comprising the following steps of:
(1) Shadow separation and removal are carried out on the collected visible light images;
(2) Performing feature recognition according to the image information with the shadow information removed to obtain names of all devices in the image and device state information;
(3) Obtaining any device Name l Belongs to category Ω l E {1,2, …, S }, retrieving a pre-stored data matrix M e R S×S The method comprises the steps of carrying out a first treatment on the surface of the Judging whether the association relation of two kinds of devices in the image meets M ij =0, if yes, deleting detection recognition results of the two types of equipment;
(4) Based on the result obtained in the step 3, issuing and displaying the cable tunnel engineering inspection alarm information;
in the step (1), separating and removing the shadow portion includes the steps of:
(11) Image I of visible light RGB ＝(L R ,L G ,L B ) Conversion from RGB color space to intrinsic color space to form intrinsic image I Int ＝(I 1 ,I 2 ,I 3 )，
I 1 ＝L R +L G -β 1 L B
I 2 ＝L R -β 2 L G +L B
I 3 ＝-β 3 L R +L G +L B
Wherein (I) 1 ,I 2 ,I 3 ) Representing eigenvalues in the eigenvalue color space; (L) R ,L G ,L B ) Representing gray values in an RGB color space; beta i I=1, 2,3, which is the conversion coefficient of RGB color space to intrinsic color space;
(12) By screening I 3 Not less than k as visible light image I RGB Is realized for visible light image I by shadow information mode RGB Shadow information I is separated from the cable tunnel engineering inspection image by detecting and separating the shadow information of the cable tunnel engineering inspection image RGB-sha And intrinsic image information I RGB-cor The method comprises the steps of carrying out a first treatment on the surface of the Wherein I is RGB ＝I RGB-cor +I RGB-sha K is a preset value;
data matrix M epsilon R S×S Storing the association relation of equipment in cable tunnel engineering, wherein S represents the class number of the equipment in the cable tunnel engineering, and M is the number if the equipment of the ith class and the equipment of the jth class can occur simultaneously in the same scene ij =1; otherwise, M ij ＝0。
2. The intelligent inspection method for cable tunnel engineering based on light and shadow separation as set forth in claim 1, wherein (beta) 1 ,β 2 ,β 3 ) = (-0.7261,0.3816,0.3316) is the empirically chosen conversion coefficient of the RGB color space into the intrinsic color space.
3. The intelligent inspection method for cable tunnel engineering based on light and shadow separation as claimed in claim 1, wherein k=65.
4. Cable tunnel engineering intelligence inspection system based on light shadow separation, its characterized in that includes:
the image sensing module is used for acquiring visible light images in the cable tunnel engineering inspection;
the shadow detection module is used for carrying out shadow separation and removal on the visible light image acquired by the image sensing module;
the joint detection module comprises a device detection and identification module, an expert priori knowledge module and a semantic logic identification module; the equipment detection and identification module is used for detecting and identifying all equipment names and equipment state information based on the evidence image information; the expert priori knowledge module is used for using the data matrix M epsilon R S×S Pre-storing the association relation of the devices in the cable tunnel engineering in a form, wherein S is the number of device categories in the cable tunnel engineering, and M is the number of the devices in the cable tunnel engineering if the ith device and the jth device can simultaneously appear in the same scene ij =1, otherwise, M ij =0; the semantic logic recognition module is used for acquiring any device Name detected and recognized by the device detection recognition module l Class Ω to which it belongs l E {1,2, …, S }, retrieving a pre-stored data matrix M e R S×S The method comprises the steps of carrying out a first treatment on the surface of the Judging whether the association relation of two kinds of devices in the image meets M ij =0, if yes, deleting detection recognition results of the two types of equipment;
the display alarm module is used for issuing and displaying the cable tunnel engineering inspection alarm information on the result obtained by the joint detection module;
the shadow detection module is used for detecting visible light image I RGB ＝(L R ,L G ,L B ) Conversion from RGB color space to intrinsic color space to form intrinsic image I Int ＝(I 1 ,I 2 ,I 3 )，
I 1 ＝L R +L G -β 1 L B
I 2 ＝L R -β 2 L G +L B
I 3 ＝-β 3 L R +L G +L B
Wherein (I) 1 ,I 2 ,I 3 ) Representing eigenvalues in the eigenvalue color space; (L) R ,L G ,L B ) Representing gray values in an RGB color space; beta i I=1, 2,3, which is the conversion coefficient of RGB color space to intrinsic color space;
for screening I 3 Not less than k as visible light image I RGB Is realized for visible light image I by shadow information mode RGB Shadow information I is separated from the cable tunnel engineering inspection image by detecting and separating the shadow information of the cable tunnel engineering inspection image RGB-sha And intrinsic image information I RGB-cor The method comprises the steps of carrying out a first treatment on the surface of the Wherein I is RGB ＝I RGB-cor +I RGB-sha K is a preset value.
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