CN115018784A - Method, device, equipment and medium for detecting defect of strand scattering of lead - Google Patents
Method, device, equipment and medium for detecting defect of strand scattering of lead Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a medium for detecting a defect of strand scattering of a lead. The method comprises the following steps: acquiring an infrared detection image and a visible light detection image in a power line scene; performing image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image, wherein pixel points in the fusion detection image comprise visible light color features and infrared gray features; inputting the fusion detection image into a pre-trained image segmentation model, and acquiring a lead mask image corresponding to the fusion detection image; and dividing an infrared lead area from the infrared detection image according to the lead mask image, and detecting whether the lead strand scattering defect exists in the power line detection area or not according to the infrared lead area. By adopting the technical scheme, the accuracy rate of the detection of the strand scattering defects of the conducting wires can be improved.
Description
Technical Field
The invention relates to the technical field of electric power detection, in particular to a method, a device, equipment and a medium for detecting a wire strand loosing defect.
Background
In an electric power system, safe transmission of electric energy is a key to stable operation of the electric power system. The wire is responsible for transmitting the electric energy in the electric wire netting, and the strand defect of the wire strand scattering easily causes the sharp increase of power line power to produce phenomena such as circuit short circuit or wire strand break. The method can accurately detect and solve the defect of strand scattering of the lead at the early stage, and can effectively prevent the lead from being further damaged, thereby ensuring the safety and stability of power transmission of the power line.
At present, the method for detecting the defect of the strand spreading of the lead can be roughly divided into two types, one type adopts a computer vision algorithm to process a visible light image, and utilizes the characteristic of the strand spreading to directly detect the strand spreading area of the lead; in the other type, the temperature threshold value is judged by using the resistance temperature rise characteristic of the strand scattering area of the conducting wire, and the strand scattering condition of the conducting wire is predicted by using a thermodynamic diagram mapped by an infrared image.
However, the method for detecting the strand scattering area of the wire in the visible light image by using the computer vision algorithm is difficult to detect the condition of slight strand scattering of the wire, and the algorithm recall rate is poor; according to the method for predicting the strand scattering condition of the wire by utilizing the thermodynamic diagram mapped by the infrared image, due to the fact that the background of the infrared image is complex, the contrast is low, the noise is large, and a large amount of misjudgments are prone to occur when the strand scattering defect of the wire is judged.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for detecting a defect of strand scattering of a wire, which are used for improving the accuracy of detecting the defect of strand scattering of the wire.
According to an aspect of the present invention, there is provided a method for detecting a strand spreading defect of a wire, the method comprising:
acquiring an infrared detection image and a visible light detection image in a power line scene;
performing image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image, wherein pixel points in the fusion detection image comprise visible light color features and infrared gray features;
inputting the fusion detection image into a pre-trained image segmentation model, and acquiring a lead mask image corresponding to the fusion detection image;
and dividing an infrared lead area from the infrared detection image according to the lead mask image, and detecting whether the lead strand scattering defect exists in the power line detection area or not according to the infrared lead area.
According to another aspect of the present invention, there is provided a device for detecting a strand spreading defect of a wire, comprising:
the image acquisition module is used for acquiring an infrared detection image and a visible light detection image in a power line scene;
the image fusion module is used for carrying out image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image, and pixel points in the fusion detection image comprise visible light color characteristics and infrared gray characteristics;
the mask image acquisition module is used for inputting the fusion detection image into a pre-trained image segmentation model and acquiring a lead mask image corresponding to the fusion detection image;
and the strand scattering defect detection module is used for segmenting an infrared lead area from the infrared detection image according to the lead mask image and detecting whether a strand scattering defect of a lead exists in the power line detection area or not according to the infrared lead area.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the method of detecting a stray wire defect according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to implement the method for detecting the strand break defect according to any one of the embodiments of the present invention when the computer instructions are executed.
The technical scheme of the embodiment of the invention obtains the infrared detection image and the visible light detection image in the scene of the power line, fusing the infrared detection image and the visible light detection image of the same frame to obtain a four-channel fused detection image, acquiring a lead mask image from the fused detection image by using an image segmentation model, segmenting an infrared lead area from the infrared detection image according to the lead mask image, calculating an abnormal temperature threshold value by using an inter-class variance method, and combining all pixel points which are larger than the abnormal temperature threshold value in the segmented infrared lead area to obtain an abnormal temperature area, thereby providing a method for accurately identifying whether the lead has strand scattering defects or not, the detection of the defects of the scattered strands of the wires is not influenced by illumination conditions, and the defects of the scattered strands of the wires can be detected in time at the early stage of the scattering of the wires, so that the wires are effectively prevented from being further damaged.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for detecting a strand spreading defect of a conducting wire according to an embodiment of the present invention;
FIG. 2a is a flowchart of a method for detecting a strand spreading defect of a conducting wire according to a second embodiment of the present invention;
fig. 2b is a structural diagram of a UNet model provided by the second embodiment of the present invention;
fig. 2c is a diagram of a residual error module structure provided by the second embodiment of the present invention;
FIG. 2d is a block diagram of a decoding module according to a second embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a device for detecting a strand scattering defect of a conducting wire according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device implementing the method for detecting a wire stranding defect according to the embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and the above-described drawings, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a method for detecting a wire stranding defect according to an embodiment of the present invention, where the method is applicable to determining whether a wire area in an image has a wire stranding defect by using an infrared image and a visible light image in an electric power line scene shot by an unmanned aerial vehicle, and the method may be executed by a device for detecting a wire stranding defect, where the device for detecting a wire stranding defect may be implemented in a form of hardware and/or software, and the device for detecting a wire stranding defect may be configured in a device having an image processing function. As shown in fig. 1, the method includes:
and S110, acquiring an infrared detection image and a visible light detection image in a power line scene.
In a specific embodiment, the infrared detection image and the visible light detection image can be obtained by shooting with an unmanned aerial vehicle, and the unmanned aerial vehicle is matched with an infrared sensor and a visible light sensor. Optionally, in the power line scenario, the drone is flying about 10 meters from the power line while taking infrared and visible light images at a low frame rate.
The infrared image detection image is obtained through an infrared sensor carried on the unmanned aerial vehicle and is a single-channel image represented by gray values. The infrared sensor maps the received heat radiated outwards from the scene (including the dynamic object, the static object and the background) into a gray value, and converts the gray value into an infrared detection image, wherein the higher the heat radiated outwards from a certain part of the scene is, that is, the higher the temperature of the certain part of the scene is, the higher the gray value of the part reflected in the infrared detection image is, the brighter the image is. The gray values typically range from 0 to 255, being integer values, 0 representing black, 255 representing white, and the intermediate values being some different level of gray.
The visible light detection image is obtained through a visible light sensor carried on the unmanned aerial vehicle, and visible light is a part which can be perceived by human eyes in an electromagnetic spectrum. The visible light detection image is an RGB mode image, the RGB mode image is composed of three color channels, which are a Red channel (Red), a Green channel (Green), and a Blue channel (Blue), respectively, and an RGB value may represent the luminance of each color, which is generally in a range of 0 to 255 and is an integer value.
The frame rate refers to the number of frames of images generated by the infrared sensor and the visible light sensor per second.
And S120, carrying out image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image, wherein pixel points in the fusion detection image comprise visible light color characteristics and infrared gray characteristics.
In the embodiment, it is considered that, under the condition that the light source is sufficient, the wire in the visible light detection image has the characteristic of being clearly distinguished from the background, however, the detection performance of the visible light sensor is limited under the conditions of dark light, glare, thick fog and the like, a clear visible light detection image cannot be obtained, the accuracy is low when the wire region and the non-wire region of the image are subsequently segmented, and the infrared sensor images according to the temperature of the object, so that the wire target with a higher temperature can be captured even under the condition of poor illumination. Therefore, in the embodiment, a mode of image fusion of the visible light detection image and the infrared detection image is adopted, so that the fused detection image simultaneously contains information in the infrared detection image and the visible light detection image, and is not influenced by illumination conditions.
The pixels are the minimum units forming the image, each minimum unit can be regarded as a pixel point, each pixel point has a definite position in the image and an assigned color value, and the color values are the gray value and the RGB value of the image in the embodiment.
The image fusion specifically means that the characteristics of each channel of the three-channel visible light RGB image and the single-channel infrared gray image of the same frame are fused to obtain a four-channel image containing visible light color characteristics and infrared gray characteristics.
The visible light color feature is a visible light RGB value feature, and the infrared gray scale feature is an infrared gray scale value feature.
The advantages of such an arrangement are: the defect that the obtained visible light image is not clear under the condition of poor lighting condition is overcome, the information contained in the fused image is more comprehensive, and the wire area and the non-wire area can be easily separated from the fused image.
And S130, inputting the fusion detection image into a pre-trained image segmentation model, and acquiring a lead mask image corresponding to the fusion detection image.
Wherein the image segmentation model may be a UNet segmentation model. The UNet segmentation model is a model trained based on a deep convolutional neural network, has high recognition accuracy and generalization capability, has high representativeness of extracted features, and overcomes the defects that the traditional image processing method needs specific background conditions or illumination conditions, is poor in real-time monitoring performance, is weak in generalization capability and the like.
Specifically, the conductive line mask map is mainly used for dividing conductive line regions and non-conductive line regions, and is a binary image composed of 0 and 1, wherein the conductive line regions are all composed of 1, and white regions are displayed in the mask map; the non-conductive line regions are each composed of 0, and are displayed as black regions in the mask diagram.
Based on the above embodiment, the present invention creatively proposes that a fusion detection image obtained by fusing a visible light detection image and an infrared detection image is input into an image segmentation model, and a wire mask image corresponding to the fusion detection image is obtained, so that the UNet model has higher accuracy when segmenting an image with poor illumination conditions.
S140, segmenting an infrared lead area from the infrared detection image according to the lead mask image, and detecting whether the lead strand scattering defect exists in the power line detection area or not according to the infrared lead area.
The step of dividing the infrared lead area from the infrared detection image according to the lead mask image specifically means that corresponding pixel points are found in the infrared detection image before image fusion corresponding to the pixel points according to pixel point positions contained in the lead area divided from the lead mask image, and an area formed by the pixel points is the infrared lead area.
According to the infrared lead area, detecting whether the strand scattering defect of the lead exists in the power line detection area, specifically, the method may include: and identifying an abnormal temperature area in the infrared wire area, and determining that the wires in the abnormal temperature area have the wire strand scattering defect.
Further, identifying an abnormal temperature region in the infrared wire region may specifically include:
acquiring an abnormal temperature threshold matched with the infrared lead region by adopting an inter-class variance method;
and combining all pixel points with the gray values larger than the abnormal temperature threshold value in the infrared conductor area to obtain the abnormal temperature area.
Further, acquiring an abnormal temperature threshold matched with the infrared lead region by using an inter-class variance method specifically comprises the following steps:
acquiring a gray level range [0, L-1] matched with the infrared lead region, wherein L is an integer larger than 1;
acquiring the number n of pixel points belonging to each gray level i in the infrared conductor region i The total pixel number of the wire area is N;
according to the formula: p is a radical of i ＝n i N, i is 0, 1, 2 …, L-1, and the probability p corresponding to each gray level i is calculated i ；
Sequentially obtaining a current processing gray level T in the gray level range, and obtaining the gray value in the infrared conductor region at [0, T]First pixel point set C 0 And the gray scale value is at [ T +1, L-1]Second set of pixel points C 1 ；
According to the formula:
calculating to obtain and C 0 Correspond to each otherValue u 0 And with C 1 Corresponding mean value u 1 (ii) a Wherein the content of the first and second substances,
ω 1 ＝1-ω 0 ；
according to the formula: u. of T ＝ω 0 u 0 +ω 1 u 1 And calculating to obtain the image mean value u of the infrared lead area T ；
According to the formula:
calculating to obtain the inter-class variance matched with the current processing gray level T
Returning to execute the operation of sequentially obtaining a current processing gray level T in the gray level range until the inter-class variance corresponding to each gray level is obtained through calculation;
and determining the target gray level corresponding to the maximum between-class variance as the abnormal temperature threshold.
On the basis of the above embodiment, it can be simply understood that the inter-class variance matching the current processing gray level T is calculated by using the parameter points and known parameters such as gray values in the image and the current processing gray level T acquired in the gray level range
At all calculated inter-class variances
In the search, find the value of
Will communicate with the
The corresponding gray level T is determined asAn abnormal temperature threshold.
The technical scheme of the embodiment of the invention obtains the infrared detection image and the visible light detection image in the scene of the power line, fusing the infrared detection image and the visible light detection image of the same frame to obtain a four-channel fused detection image, acquiring a lead mask image from the fused detection image by using an image segmentation model, segmenting an infrared lead area from the infrared detection image according to the lead mask image, calculating an abnormal temperature threshold value by using an inter-class variance method, and combining all pixel points which are larger than the abnormal temperature threshold value in the segmented infrared lead area to obtain an abnormal temperature area, thereby providing a method for accurately identifying whether the lead has strand scattering defects or not, the detection of the defects of the scattered strands of the wires is not influenced by illumination conditions, and the defects of the scattered strands of the wires can be detected in time at the early stage of the scattering of the wires, so that the wires are effectively prevented from being further damaged.
Example two
Fig. 2a is a flowchart of a method for detecting a wire stranding defect according to a second embodiment of the present invention, and the present embodiment further embodies a detection flow of the wire stranding defect on the basis of the above embodiment. As shown in fig. 2a, the method comprises:
s210, obtaining an original UNet model to be trained.
Fig. 2b shows a block diagram of the UNet model. As shown in fig. 2b, the UNet model is composed of an encoding network and a decoding network, the left half of fig. 2b is from the input to the lowest Res Block as the encoding network, the right half of fig. 2b is from the lowest Decode Block to the output as the decoding network, and the whole network is downsampled 5 times and upsampled 5 times. Each downsampling reduces the size of the feature map by half, the number of channels is doubled, and the feature map is changed from a flat shape to be small and dense and is more three-dimensional; the size of the feature map is doubled in each upsampling, the number of channels is halved, and the feature map is finally restored to the size of the input.
Wherein, the coding network of the original UNet model comprises a ResNet50 network with 6 cross-layer connections; the decoding network of the original UNet model comprises 5 decoding modules, and 4 jump connections are arranged between the coding network and the decoding network.
The coding network of the original UNet model adopts a ResNet50 network with 6 cross-layer connections, and the advantage of the arrangement is that: because the semantics of the lead are simple, and the edge detail information of the lead is difficult to capture, the deep semantic information and the shallow feature detail of the image are important, 6 cross-layer connections are added on the basis of ResNet50, the dimensionality is unified through maximum pooling operation (Maxpool), a dense network structure is formed, the shallow feature can be reused by the structure, the deep extension of the feature is enhanced, and meanwhile, the gradient disappearance during reverse propagation can be prevented through the cross-layer connections.
The coding network and the decoding network have 4 jump connections, and the arrangement has the advantages that: and features with the same dimensionality are spliced by using a jump connection mode, so that multiplexing of shallow features is realized.
The coding network comprises 4 residual modules (Res Block), the structure of the residual modules is shown in fig. 2c, each module comprises two Convolution layers (Conv), an activated function layer (ReLU) and two Batch Normalization layers (BN), and the Convolution kernel size of the Convolution layer is 3 × 3.
The decoding network includes five decoding modules (Decode Block), and the decoding module structure is shown in fig. 2d, and each module includes an upsampling (Up Sample), two convolutional layers Conv2d, and two active function layers ReLU. The up-sampling method adopts a nearest neighbor interpolation method for doubling the size of the feature map, the convolution kernel is 3 x 3 in size, the step length is 1, and the feature is fused.
And S220, defining a loss function.
The loss function L dice The calculation formula is as follows:
the loss function adopts a Dice function, wherein W is the image width, H is the image height, P is the model output image, and P is ij Is a pixel point in P, T is a training sample image, T ij Is in TA pixel point of (2).
The benefit of this arrangement is: in the electric power line image, the number of background negative samples is far larger than that of wire positive samples, a Dice function is commonly used for calculating the similarity between samples, and the Dice function focuses on whether the foreground image is classified correctly or not, does not focus on background pixels, and can effectively solve the problem of unbalance of the foreground and background samples by adopting the Dice function as a loss function.
And S230, carrying out iterative training on the original UNet model by using a pre-labeled training sample set to obtain a UNet segmentation model.
The training sample set is a fusion image which is marked with matched conducting wire mask patterns in a plurality of electric power scenes in advance, and the fusion image is an image formed by fusing an infrared image shot by an unmanned aerial vehicle and a visible light image.
The specific iterative training method comprises the following steps: training a UNet model on a physical computer platform Windows, wherein the model training adopts an adaptive moment estimation optimizer as a training optimization strategy of a network, and training is carried out for 100 times. The learning rate is dynamically adjusted from 0.001, and after each round of updating is completed, the learning rate is multiplied by 0.9. And observing the change of the training loss function, stopping training when the loss function value is not reduced for 5 continuous turns, and obtaining a model with the best convergence effect, namely the model with the lowest final loss function value.
S240, acquiring an infrared detection image and a visible light detection image in the power line scene.
And S250, carrying out image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image.
It should be noted that each group of infrared monitoring images and visible light images used for image fusion must be images of the same frame, that is, pixel points at the same position in each group of infrared monitoring images and visible light images correspond to the same position point in the real space.
And S260, inputting the fusion detection image into a pre-trained UNet model, and acquiring a lead mask image corresponding to the fusion detection image.
And S270, segmenting an infrared lead area from the infrared detection image according to the lead mask image.
Specifically, according to the positions of pixel points included in the lead area divided in the lead mask image, corresponding pixel points are found in the corresponding infrared detection image before image fusion, and the area formed by the pixel points is the infrared lead area.
And S280, acquiring an abnormal temperature threshold matched with the infrared lead region by adopting an inter-class variance method.
And S290, comparing the gray value in the infrared conductor region with the abnormal temperature threshold, and combining the pixel points of which the gray value is greater than the abnormal temperature threshold in the infrared conductor region to obtain the abnormal temperature region.
And S2100, judging the abnormal temperature area to be an area with the wire strand scattering defect.
According to the technical scheme of the embodiment of the invention, the UNet segmentation model is trained in advance, the fusion detection image is input into the UNet segmentation model to obtain the lead mask image, the infrared lead area is segmented from the lead mask image, and the abnormal temperature area in the infrared lead area is judged to be the area with the lead strand scattering defect, so that the problem that the contrast ratio of the lead and the background in the infrared image is low and difficult to distinguish is solved, the false alarm rate of lead strand scattering detection is reduced, and the influence of the illumination condition on the lead strand scattering detection is eliminated.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a device for detecting a strand scattering defect of a conducting wire according to a third embodiment of the present invention. As shown in fig. 3, the apparatus includes: an image acquisition module 310, an image fusion module 320, a mask image acquisition module 330, and a splay defect detection module 340, wherein:
the image acquiring module 310 is configured to acquire an infrared detection image and a visible light detection image in a power line scene.
The image fusion module 320 is configured to perform image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image, where a pixel point in the fusion detection image includes a visible light color feature and an infrared gray feature.
The mask map obtaining module 330 is configured to input the fusion detection image into a pre-trained image segmentation model, and obtain a wire mask map corresponding to the fusion detection image.
And the strand scattering defect detecting module 340 is configured to segment an infrared lead region from the infrared detection image according to the lead mask diagram, and detect whether a strand scattering defect of a lead exists in the power line detection region according to the infrared lead region.
The technical scheme of the embodiment of the invention obtains the infrared detection image and the visible light detection image in the scene of the power line, fusing the infrared detection image and the visible light detection image of the same frame to obtain a four-channel fused detection image, acquiring a lead mask image from the fused detection image by using an image segmentation model, segmenting an infrared lead area from the infrared detection image according to the lead mask image, calculating an abnormal temperature threshold value by using an inter-class variance method, and combining all pixel points which are larger than the abnormal temperature threshold value in the segmented infrared lead area to obtain an abnormal temperature area, thereby providing a method for accurately identifying whether the lead has strand scattering defects or not, the detection of the defect of the strand spreading of the wire is not influenced by illumination conditions, and the strand spreading of the wire can be detected in time at the early stage of the strand spreading of the wire, so that the wire is effectively prevented from being further damaged.
In addition to the above embodiments, the image segmentation model is a UNet segmentation model.
On the basis of the foregoing embodiments, the image obtaining module 310 may be specifically configured to: and acquiring an infrared detection image and a visible light detection image acquired at a low frame rate when the unmanned aerial vehicle flies away from the preset height of the power line scene.
On the basis of the foregoing embodiments, the strand fault detection module 340 may be specifically configured to: and identifying an abnormal temperature area in the infrared wire area, and determining that the wires in the abnormal temperature area have the wire strand scattering defect.
On the basis of the foregoing embodiments, the system may further include a UNet model training module, configured to:
acquiring an original UNet model to be trained; the encoding network of the original UNet model comprises a ResNet50 network with 6 cross-layer connections; the decoding network of the original UNet model comprises 5 decoding modules, and 4 jump connections are arranged between the coding network and the decoding network;
using a pre-labeled training sample set to carry out iterative training on the original UNet model to obtain the UNet segmentation model;
wherein, in the course of training the UNet model, a Dice function is used as a loss function.
On the basis of the above embodiments, the method may further include: an abnormal temperature region detection unit comprising:
the abnormal temperature threshold acquiring subunit is used for acquiring an abnormal temperature threshold matched with the infrared lead region by adopting an inter-class variance method;
and the abnormal temperature region acquisition subunit is used for combining all the pixel points with the gray values larger than the abnormal temperature threshold value in the infrared conductor region to obtain the abnormal temperature region.
On the basis of the foregoing embodiments, the abnormal temperature threshold acquisition subunit may be specifically configured to:
acquiring a gray level range [0, L-1] matched with the infrared lead region, wherein L is an integer larger than 1;
acquiring the number n of pixel points belonging to each gray level i in the infrared conductor region i The total pixel number of the wire area is N;
according to the formula: p is a radical of i ＝n i N, i is 0, 1, 2 …, L-1, and the probability p corresponding to each gray level i is calculated i ；
Sequentially obtaining a current processing gray level T in the gray level range, and obtaining the gray value in the infrared conductor region at [0, T]First pixel point set C 0 And the gray scale value is at [ T +1, L-1]Second set of pixel points C 1 ；
According to the formula:
calculating to obtain and C 0 Corresponding mean value u 0 And with C 1 Corresponding mean value u 1 (ii) a Wherein the content of the first and second substances,
ω 1 ＝1-ω0；
according to the formula: u. of T ＝ω 0 u 0+ ω 1 u 1 And calculating to obtain the image mean value u of the infrared lead area T ；
According to the formula:
calculating to obtain the inter-class variance matched with the current processing gray level T
Returning to execute the operation of sequentially obtaining a current processing gray level T in the gray level range until the inter-class variance corresponding to each gray level is obtained through calculation;
and determining the target gray level corresponding to the maximum between-class variance as the abnormal temperature threshold.
The detection device for the wire strand scattering defect provided by the embodiment of the invention can execute the detection method for the wire strand scattering defect provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example 4
FIG. 4 shows a schematic block diagram of an electronic device 40 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a Read Only Memory (ROM)42, a Random Access Memory (RAM)43, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 41 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)42 or the computer program loaded from the storage unit 48 into the Random Access Memory (RAM) 43. In the RAM 43, various programs and data necessary for the operation of the electronic apparatus 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other via a bus 44. An input/output (I/O) interface 45 is also connected to bus 44.
A number of components in the electronic device 40 are connected to the I/O interface 45, including: an input unit 46 such as a keyboard, a mouse, etc.; an output unit 47 such as various types of displays, speakers, and the like; a storage unit 48 such as a magnetic disk, optical disk, or the like; and a communication unit 49 such as a network card, modem wireless communication transceiver, or the like. The communication unit 49 allows the electronic device 40 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
Namely:
acquiring an infrared detection image and a visible light detection image in a power line scene;
performing image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image, wherein pixel points in the fusion detection image comprise visible light color characteristics and infrared gray level characteristics;
inputting the fusion detection image into a pre-trained image segmentation model, and acquiring a lead mask image corresponding to the fusion detection image;
and dividing an infrared lead area from the infrared detection image according to the lead mask image, and detecting whether the lead strand scattering defect exists in the power line detection area or not according to the infrared lead area.
In some embodiments, the method of detecting a strand break defect in a wire may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 40 via the ROM 42 and/or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method of detecting a strand break defect described above may be performed. Alternatively, in other embodiments, the processor 41 may be configured by any other suitable means (e.g., by means of firmware) to perform the method of detection of a strand break defect.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for detecting a wire strand scattering defect is characterized by comprising the following steps:
acquiring an infrared detection image and a visible light detection image in a power line scene;
performing image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image, wherein pixel points in the fusion detection image comprise visible light color features and infrared gray features;
inputting the fusion detection image into a pre-trained image segmentation model, and acquiring a lead mask image corresponding to the fusion detection image;
and dividing an infrared lead area from the infrared detection image according to the lead mask image, and detecting whether the lead strand scattering defect exists in the power line detection area or not according to the infrared lead area.
2. The method of claim 1, wherein acquiring the infrared detection image and the visible detection image in the power line scene comprises:
and acquiring an infrared detection image and a visible light detection image acquired at a low frame rate when the unmanned aerial vehicle flies away from the preset height of the power line scene.
3. The method of claim 1, wherein the image segmentation model is a UNet segmentation model.
4. The method of claim 3, further comprising, prior to inputting the fused detection image into a pre-trained image segmentation model:
acquiring an original UNet model to be trained; the encoding network of the original UNet model comprises a ResNet50 network with 6 cross-layer connections; the decoding network of the original UNet model comprises 5 decoding modules, and 4 jump connections are arranged between the coding network and the decoding network;
using a pre-labeled training sample set to carry out iterative training on the original UNet model to obtain the UNet segmentation model;
wherein, in the course of training the UNet model, a Dice function is used as a loss function.
5. The method of any one of claims 1-4, wherein detecting whether a strand break defect exists in the power line detection area based on the infrared wire area comprises:
and identifying an abnormal temperature area in the infrared wire area, and determining that the wires in the abnormal temperature area have the wire strand scattering defect.
6. The method of claim 5, wherein identifying an abnormal temperature zone in the infrared wire zone comprises:
acquiring an abnormal temperature threshold matched with the infrared lead region by adopting an inter-class variance method;
and combining all the pixel points with the gray values larger than the abnormal temperature threshold value in the infrared conductor area to obtain the abnormal temperature area.
7. The method of claim 6, wherein obtaining the abnormal temperature threshold matched with the infrared wire region by using an inter-class variance method comprises:
acquiring a gray level range [0, L-1] matched with the infrared lead region, wherein L is an integer larger than 1;
acquiring the number ni of pixel points belonging to each gray level i in the infrared lead area, wherein the total pixel number of the infrared lead area is N;
according to the formula: p is a radical of i ＝n i N, i is 0, 1, 2 …, L-1, and the probability p corresponding to each gray level i is calculated i ；
Sequentially obtaining a current processing gray level T in the gray level range, and obtaining the gray value in the infrared conductor region at [0, T]First pixel point set C 0 And the gray scale value is at [ T +1, L-1]Second set of pixel points C 1 ；
According to the formula:
calculating to obtain and C 0 Corresponding mean value u 0 And with C 1 Corresponding mean value u 1 (ii) a Wherein the content of the first and second substances,
ω 1 ＝1-ω 0 ；
according to the formula: u. u T ＝ω 0 u 0 +ω 1 u 1 And calculating to obtain the image mean value u of the infrared lead area T ；
According to the formula:
calculating to obtain the inter-class variance matched with the current processing gray level T
Returning to execute the operation of sequentially obtaining a current processing gray level T in the gray level range until the inter-class variance corresponding to each gray level is obtained through calculation;
and determining the target gray level corresponding to the maximum between-class variance as the abnormal temperature threshold.
8. A detection device for a wire strand scattering defect is characterized by comprising:
the image acquisition module is used for acquiring an infrared detection image and a visible light detection image in a power line scene;
the image fusion module is used for carrying out image fusion on the infrared detection image and the visible light detection image to obtain a fusion detection image, and pixel points in the fusion detection image comprise visible light color characteristics and infrared gray characteristics;
the mask image acquisition module is used for inputting the fusion detection image into a pre-trained image segmentation model and acquiring a lead mask image corresponding to the fusion detection image;
and the strand scattering defect detection module is used for segmenting an infrared lead area from the infrared detection image according to the lead mask image and detecting whether a strand scattering defect of a lead exists in the power line detection area or not according to the infrared lead area.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method of detecting a stray strand defect of a wire according to any one of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions for causing a processor to implement the method of detecting a stray strand defect of a wire according to any one of claims 1 to 7 when the computer instructions are executed.
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