CN111488820A - Cable tunnel engineering intelligent inspection method and system based on light and shadow separation - Google Patents
Cable tunnel engineering intelligent inspection method and system based on light and shadow separation Download PDFInfo
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Abstract
The invention discloses a cable tunnel engineering intelligent inspection method based on light and shadow separation, which comprises the steps of carrying out shadow separation and removal on collected visible light images; performing feature recognition according to the image information from which the shadow information is removed to obtain names and equipment state information of all equipment in the image; judging whether the incidence relation of two types of equipment in the image meets MijIf yes, deleting the detection and identification results of the two types of equipment; and releasing and displaying the routing inspection alarm information of the cable tunnel engineering. The invention also provides a cable tunnel based on light and shadow separationThe intelligent engineering inspection system comprises an image sensing module, a shadow detection module, a joint detection module and a display alarm module, and is used for issuing and displaying inspection alarm information of the cable tunnel engineering 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 a cable tunnel engineering intelligent inspection method and system based on light and shadow separation, and belongs to the technical field of electric power operation inspection.
Background
In the prior art, the overhaul of cables and supporting equipment in tunnels is mainly completed manually, and the problems of a large amount of dense smoke, harmful gas, high-voltage cable leakage, cable tunnel collapse and the like in tunnels can threaten the life safety of workers, along with the development of the robot technology and the image recognition technology, part of experts and scholars propose that inspection robots are used for detecting the internal condition of the cable tunnels and gradually become an effective solution, for example, the implementation and application of robots in inspection of cable tunnels disclosed in the Chinese journal document electronic technology and software engineering (pages 17 to 74 in 2019), and the observation of a migration learning neural network-based cable tunnel recognition algorithm disclosed in the Chinese journal document China (pages 52 to 104 in 2019), while the natural lighting of the cables is a complicated shadow removal algorithm for recognizing the observation of a migration learning neural network-based on the migration learning neural network, and the defect of a complicated shadow of a traditional observation of a traditional shadow image collection process, such as the false shadow removal of a traditional shadow image collection of a traditional color image, a traditional gradient shadow image recognition algorithm for removing is used as a traditional shadow image collection method for removing the complicated shadow of a traditional shadow, and a traditional shadow image collection of a traditional color image, and a traditional shadow image collection method for removing the traditional shadow of a traditional gradient shadow of a traditional shadow under the traditional gradient shadow recognition method for the traditional combined shadow of a traditional shadow recognition method, such as a traditional shadow detection of a traditional shadow recognition method for removing the traditional shadow, a traditional shadow detection of a traditional shadow recognition method for the traditional shadow detection of a traditional shadow recognition method for the traditional shadow recognition method for removing the traditional shadow of a traditional shadow recognition method for the traditional shadow of a traditional shadow recognition method for the traditional shadow recognition of a traditional shadow recognition method for the traditional shadow recognition of a traditional shadow recognition method for the traditional shadow.
Disclosure of Invention
Therefore, the invention aims to provide a method and a system for intelligently patrolling cable tunnel engineering based on light and shadow separation, which realize the accuracy improvement and false alarm rate suppression of intelligent patrolling and provide technical support for the overall improvement of the intelligent patrolling capability of the cable tunnel engineering.
In order to achieve the purpose, the invention discloses an intelligent inspection method for cable tunnel engineering based on light and shadow separation, which comprises the following steps:
(1) carrying out shadow separation and removal on the collected visible light image;
(2) performing feature recognition according to the image information from which the shadow information is removed to obtain names and equipment state information of all equipment in the image;
(3) obtaining Name of any equipmentlClass Ωl∈ {1,2, …, S }, and retrieving a pre-stored data matrix M ∈ RS×S(ii) a Judging whether the incidence relation of two types of equipment in the image meets MijIf yes, deleting the detection and identification results of the two types of equipment;
wherein, the data matrix M ∈ RS×SStoring the association relation of the equipment in the cable tunnel engineering, wherein S represents the equipment category number in the cable tunnel engineering, and if the ith equipment and the jth equipment can appear in the same scene at the same time, Mij1 is ═ 1; otherwise, then Mij＝0；
(4) And (4) issuing and displaying the routing inspection alarm information of the cable tunnel engineering based on the result obtained in the step (3).
In the step 1, the separating and removing the shadow part comprises the following steps:
(11) visible light image IRGB＝(LR,LG,LB) Converting from RGB color space to intrinsic color space to form intrinsic image IInt＝(I1,I2,I3)，
I1＝LR+LG-β1LB
I2＝LR-β2LG+LB
I3＝-β3LR+LG+LB
Wherein (I)1,I2,I3) Representing eigenvalues in an eigencolor space (L)R,LG,LB) Representing RGBGray value in color space βiThe conversion coefficient from the RGB color space to the intrinsic color space is 1,2, 3;
(12) by screening I3≧ k as visible light image IRGBThe shadow information of the image processing system realizes the visual light image IRGBThe shadow information is detected and separated, and then the cable tunnel engineering is patrolled and examined the image and is separated the shadow information IRGB-shaAnd intrinsic image information IRGB-cor(ii) a Wherein, IRGB＝IRGB-cor+IRGB-shaAnd k is a preset value.
(β1,β2,β3) (-0.7261,0.3816,0.3316) is an empirically chosen conversion factor of the RGB color space to the intrinsic color space.
k＝65。
The invention also provides a cable tunnel engineering intelligent inspection system based on light and shadow separation, which comprises:
the image sensing module is used for acquiring a visible light image in the cable tunnel engineering inspection;
the shadow detection module is used for carrying out shadow separation and removal on the visible light image collected by the image sensing module;
the joint detection module comprises an equipment detection and identification module, an expert priori knowledge module and a semantic logic identification module, wherein 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, and the expert priori knowledge module is used for obtaining a data matrix M ∈ RS×SPre-storing the association relationship of the equipment in the cable tunnel engineering in a form, wherein S is the number of the equipment types in the cable tunnel engineering, and if the ith equipment and the jth equipment can appear in the same scene at the same time, M isijIf not, then Mij0; the semantic logic identification module is used for acquiring any equipment Name detected and identified by the equipment detection identification modulelTo the category omegal∈ {1,2, …, S }, and retrieving a pre-stored data matrix M ∈ RS ×S(ii) a Judging whether the incidence relation of two types of equipment in the image meets MijIf yes, delete itDetecting and identifying results of the two types of equipment;
and the display alarm module is used for issuing and displaying the routing inspection alarm information of the cable tunnel engineering according to the result obtained by the joint detection module.
A shadow detection module for detecting the visible light image IRGB＝(LR,LG,LB) Converting from RGB color space to intrinsic color space to form intrinsic image IInt＝(I1,I2,I3)，
I1＝LR+LG-β1LB
I2＝LR-β2LG+LB
I3＝-β3LR+LG+LB
Wherein (I)1,I2,I3) Representing eigenvalues in an eigencolor space (L)R,LG,LB) Representing gray values in RGB color space βiThe conversion coefficient from the RGB color space to the intrinsic color space is 1,2, 3;
and for screening I3≧ k as visible light image IRGBThe shadow information of the image processing system realizes the visual light image IRGBThe shadow information is detected and separated, and then the cable tunnel engineering is patrolled and examined the image and is separated the shadow information IRGB-shaAnd intrinsic image information IRGB-cor(ii) a Wherein, IRGB＝IRGB-cor+IRGB-shaAnd k is a preset value.
Compared with the prior art, the invention has the following beneficial effects:
by adopting the technical scheme, the intelligent cable tunnel engineering inspection method and system based on light and shadow separation can effectively improve the intelligent cable tunnel engineering inspection accuracy by separating and removing the shadow part in the original image and identifying the rest part; and the semantic relevance of the target detection and identification result is verified by using expert priori knowledge, so that the false alarm rate of intelligent routing inspection of the cable tunnel engineering can be effectively inhibited.
Drawings
Fig. 1 is a flowchart of the intelligent routing inspection method for cable tunnel engineering based on light and shadow separation.
Fig. 2 is a structural 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 figures and the detailed description.
The embodiment of the invention discloses a cable tunnel engineering intelligent inspection method and system based on light and shadow separation. In order to eliminate the interference of the light and shadow overlapping on the algorithm precision in the images collected in the tunnel, the equipment body and the shadow in the cable tunnel engineering are separated firstly, and then the precision improvement and the false alarm rate suppression of intelligent routing inspection are realized by connecting two modules in series by utilizing the semantic relevance of the target detection and identification result. Taking a 110KV cable tunnel of a XXX power transmission line as an example, the method is implemented through the following steps from S1 to S6:
(S1) the visible light image I of the target device collected by the image sensing module C1 is processed by the following formulaRGB＝(LR,LG,LB) Converting from RGB color space to intrinsic color space to form intrinsic image IInt＝(I1,I2,I3)。
I1＝LR+LG-β1LB
I2＝LR-β2LG+LB
I3＝-β3LR+LG+LB
Wherein (I)1,I2,I3) Representing eigenvalues in an eigencolor space (L)R,LG,LB) Representing gray scale values in the RGB color space (β)1,β2,β3) (-0.7261,0.3816,0.3316) is the conversion coefficient of the RGB color space to the intrinsic color space, which is empirically chosen in advance.
(S2) screening I Using the shadow detection Module C2365 or more as a visible light image IRGBThe shadow information mode realizes the detection and separation of the shadow information, and then removes the shadow information I from the cable tunnel engineering routing inspection imageRGB-shaAnd intrinsic image information IRGB-cor(ii) a Wherein, IRGB＝IRGB-cor+IRGB-sha。
(S3) intrinsic image information I is detected by the device detection/identification module C31RGB-corDetecting and identifying the device Name of the first devicel'Cable splice', position information (x)l max,yl max,xl min,yl min) (125,48,107,39), device state Statusl'breakage'.
(S4) using expert prior knowledge module C32 to obtain data matrix M ∈ RS×SStoring the association relationship of the equipment in the cable tunnel engineering; wherein S represents the equipment category number in the cable tunnel engineering; if the ith type device and the jth type device may appear in the same scene at the same time, Mij1 is ═ 1; otherwise, then Mij＝0。
For example as shown in the following table:
(S5) determining any one device Name by using semantic logic recognition module C33lClass Ωl∈ {1,2, …, S }, and further according to the data matrix M ∈ R stored in step S4S×SJudging the intrinsic image information IRGB-corWhether the incidence relation of certain two types of equipment exists in the system meets Mij0; if M is presentijAnd if the result is 0, deleting the detection and identification results of the two types of equipment. For example, the 'cable' is judged to belong to the first type of equipment, and the 'switch cabinet' in the same scene with the 'cable' is detected and identified by the equipment detection and identification module C31If the cabinet belongs to the second type of equipment, the incidence relation between the first type of equipment and the second type of equipment can be obtained through the table to meet MijIf the result is 0, the error exists in the identification, and the detection identification results of the two types of equipment are deleted.
(S6) the warning module C4 is used for releasing and displaying the patrol warning information of the cable tunnel engineering based on the result obtained in the step S5. For example, "cable joint" broken in XXX transmission line 110KV cable tunnel "needs to be repaired as soon as possible. Meanwhile, the original image is visually displayed in the form of a frame according to the device coordinate information output in step S3.
Fig. 2 is a diagram of an intelligent inspection system for cable tunnel engineering based on light and shadow separation according to an embodiment of the present invention. The system is schematically shown to comprise: the device comprises an image sensing module, a shadow detection module, a joint detection module and a display alarm module. Taking a 110KV cable tunnel of a XXX power transmission line as an example, the detailed functions of the specific modules are as follows:
the image sensing module C1 supports the acquisition function of visible light image data of the internal cable, cable joint, switch cabinet and other devices of the 110KV cable tunnel engineering of the power transmission line through the visible light sensor to which it belongs.
The shadow detection module C2 supports the detection and separation of shadow information in the cable tunnel engineering inspection image collected by the image sensing module C1, and then removes the shadow information from the cable tunnel engineering inspection image so as to extract the intrinsic image information of the cable tunnel engineering inspection image.
The joint detection module C3 supports the function realization of the equipment detection and identification module C31, the expert priori knowledge module C32 and the semantic logic identification module C33, wherein the equipment detection and identification module C31 is used for detecting and identifying the position and the working condition of equipment in the cable tunnel engineering polling image based on intrinsic image information and outputting an equipment name, an equipment state and an equipment coordinate in an XM L file format, the expert priori knowledge module C32 is used for storing the association relationship of the equipment in the cable tunnel engineering, and the semantic logic identification module C33 judges and filters false alarm events based on the association relationship of the equipment in the cable tunnel engineering stored by the expert priori knowledge module C32.
The display alarm module C4 issues and displays a cable tunnel engineering inspection alarm message "the $ equipment name $ $ equipment state $in110 KV cable tunnel of XXX transmission line $, $ needs to be repaired as soon as possible" according to the result obtained by the joint detection module C3. For example, "cable joint" broken in XXX transmission line 110KV cable tunnel "needs to be repaired as soon as possible. Meanwhile, the device coordinate information output by the joint detection module C3 is visually displayed in the original image in the form of a frame.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (7)
1. The cable tunnel engineering intelligent inspection method based on light and shadow separation is characterized by comprising the following steps of:
(1) carrying out shadow separation and removal on the collected visible light image;
(2) performing feature recognition according to the image information from which the shadow information is removed to obtain names and equipment state information of all equipment in the image;
(3) obtaining Name of any equipmentlClass Ωl∈ {1,2, …, S }, and retrieving a pre-stored data matrix M ∈ RS×S(ii) a Judging whether the incidence relation of two types of equipment in the image meets MijIf yes, deleting the detection and identification results of the two types of equipment;
(4) and (4) issuing and displaying the routing inspection alarm information of the cable tunnel engineering based on the result obtained in the step (3).
2. The intelligent cable tunneling inspection method based on light and shadow separation as claimed in claim 1, wherein the data matrix M ∈ RS×SAssociation of equipment in storage cable tunnel engineeringS represents the number of equipment types in the cable tunnel engineering, and if the ith equipment and the jth equipment can appear simultaneously in the same scene, M isij1 is ═ 1; otherwise, then Mij＝0。
3. The intelligent inspection method for cable tunnel engineering based on light and shadow separation as claimed in claim 1, wherein in the step (1), the separating and removing the shadow part comprises the steps of:
(11) visible light image IRGB＝(LR,LG,LB) Converting from RGB color space to intrinsic color space to form intrinsic image IInt＝(I1,I2,I3)，
I1＝LR+LG-β1LB
I2＝LR-β2LG+LB
I3＝-β3LR+LG+LB
Wherein (I)1,I2,I3) Representing eigenvalues in an eigencolor space (L)R,LG,LB) Representing gray values in RGB color space βiThe conversion coefficient from the RGB color space to the intrinsic color space is 1,2, 3;
(12) by screening I3≧ k as visible light image IRGBThe shadow information of the image processing system realizes the visual light image IRGBThe shadow information is detected and separated, and then the cable tunnel engineering is patrolled and examined the image and is separated the shadow information IRGB-shaAnd intrinsic image information IRGB-cor(ii) a Wherein, IRGB＝IRGB-cor+IRGB-shaAnd k is a preset value.
4. The intelligent inspection method for cable tunnel engineering based on light and shadow separation as claimed in claim 3, wherein (β)1,β2,β3) (-0.7261,0.3816,0.3316) is an empirically chosen conversion factor of the RGB color space to the intrinsic color space.
5. The intelligent inspection method for cable tunnel engineering based on light and shadow separation as claimed in claim 3, wherein k is 65.
6. The utility model provides a cable tunnel engineering intelligence system of patrolling and examining based on light and shadow separation which characterized in that includes:
the image sensing module is used for acquiring a visible light image in the cable tunnel engineering inspection;
the shadow detection module is used for carrying out shadow separation and removal on the visible light image collected by the image sensing module;
the joint detection module comprises an equipment detection and identification module, an expert priori knowledge module and a semantic logic identification module, wherein 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, and the expert priori knowledge module is used for obtaining a data matrix M ∈ RS×SPre-storing the association relationship of the equipment in the cable tunnel engineering in a form, wherein S is the number of the equipment types in the cable tunnel engineering, and if the ith equipment and the jth equipment can appear in the same scene at the same time, M isijIf not, then Mij0; the semantic logic identification module is used for acquiring any equipment Name detected and identified by the equipment detection identification modulelTo the category omegal∈ {1,2, …, S }, and retrieving a pre-stored data matrix M ∈ RS×S(ii) a Judging whether the incidence relation of two types of equipment in the image meets MijIf yes, deleting the detection and identification results of the two types of equipment;
and the display alarm module is used for issuing and displaying the routing inspection alarm information of the cable tunnel engineering according to the result obtained by the joint detection module.
7. The intelligent cable tunneling inspection system based on light and shadow separation as claimed in claim 6, wherein: the shadow detection module is used for detecting the visible light image IRGB＝(LR,LG,LB) Conversion from RGB color space toAn intrinsic color space forming an intrinsic image IInt＝(I1,I2,I3)，
I1＝LR+LG-β1LB
I2＝LR-β2LG+LB
I3＝-β3LR+LG+LB
Wherein (I)1,I2,I3) Representing eigenvalues in an eigencolor space (L)R,LG,LB) Representing gray values in RGB color space βiThe conversion coefficient from the RGB color space to the intrinsic color space is 1,2, 3;
and for screening I3≧ k as visible light image IRGBThe shadow information of the image processing system realizes the visual light image IRGBThe shadow information is detected and separated, and then the cable tunnel engineering is patrolled and examined the image and is separated the shadow information IRGB-shaAnd intrinsic image information IRGB-cor(ii) a Wherein, IRGB＝IRGB-cor+IRGB-shaAnd k is a preset value.
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