CN113538411A - Insulator defect detection method and device - Google Patents
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Abstract
The application discloses a method and a device for detecting insulator defects, wherein the method comprises the following steps: collecting an insulator image to be detected; inputting an insulator image to be detected into an insulator positioning network to obtain an insulator region to be detected in the insulator image to be detected and confidence coefficients of the insulator region to be detected; cutting the insulator region to be tested with the confidence coefficient larger than the confidence coefficient threshold value; and inputting the cut image of the insulator region to be detected into an insulator defect detection network, and identifying the defect region, the defect type and the confidence coefficient of the defect type of the insulator to be detected. The method and the device can be used for rapidly and accurately detecting common defects.
Description
Technical Field
The application relates to the technical field of defect detection, in particular to a method and a device for detecting insulator defects.
Background
Most of the research at present focuses on image positioning detection of key components of power transmission lines such as insulators, and detection accuracy and speed are improved from various aspects, but existing states of the power transmission line components are not detected. Other researches on the state of the component are limited to specific scenes (such as single defect, single insulator, single image background and the like), the robustness is not strong, and certain practical application capability is lacked.
For defect detection, there are generally two ways: classification and object localization. The insulator is divided into a defective type and a non-defective type, and a two-classification is carried out, and related researches show that although the method can detect defects, the false detection rate is high, various defect types cannot be distinguished in detail, and further practical application is limited.
Disclosure of Invention
The embodiment of the application provides an insulator defect detection method and device, so that common defects can be quickly and accurately detected.
In view of the above, a first aspect of the present application provides a method for detecting insulator defects, the method including:
collecting an insulator image to be detected;
inputting the insulator image to be detected into an insulator positioning network to obtain an insulator region to be detected in the insulator image to be detected and the confidence coefficient of the insulator region to be detected;
cutting the insulator region to be tested with the confidence coefficient larger than the confidence coefficient threshold value;
and inputting the cut image of the insulator region to be detected into an insulator defect detection network, and identifying the defect region, the defect type and the confidence coefficient of the defect type of the insulator to be detected.
Optionally, the method further includes:
and displaying the insulator region to be detected, the confidence coefficient of the insulator region to be detected, the defect region of the insulator to be detected, the defect type and the confidence coefficient of the defect type in the insulator region to be detected with the defect.
Optionally, the method further includes: before inputting the insulator image to be detected into an insulator positioning network to obtain the insulator region to be detected in the insulator image to be detected and the confidence coefficient of the insulator region to be detected, the method further comprises the following steps:
acquiring a plurality of data sets containing images of insulators, wherein the insulator types in the data sets comprise ceramics, glass and composite insulators;
marking the insulators in the data set;
dividing the data set into a training set, a verification set and a test set;
and iteratively training the insulator positioning network to obtain the trained insulator positioning network.
Optionally, the method further includes: before the step of inputting the cut image of the insulator region to be detected into the insulator defect detection network, the method further comprises the following steps:
cutting the insulators in the image containing the insulator region output by the insulator positioning network to form a training data set of the defect detection network;
marking the defect positions in the training data set;
and training the defect detection network to obtain the trained defect detection network.
Optionally, the Adam algorithm is adopted to perform optimization calculation on the insulator positioning network or the insulator defect detection network.
This application second aspect provides an insulator defect detecting device, the device includes:
the acquisition unit is used for acquiring an image of the insulator to be detected;
the insulator positioning unit is used for inputting the insulator image to be detected into an insulator positioning network to obtain an insulator area to be detected in the insulator image to be detected and the confidence coefficient of the insulator area to be detected;
the cutting unit is used for cutting the insulator region to be detected with the confidence coefficient larger than the confidence coefficient threshold value;
and the defect identification unit is used for inputting the cut image of the insulator region to be detected into the insulator defect detection network and identifying the defect region, the defect type and the confidence coefficient of the defect type of the insulator to be detected.
Optionally, the method further includes:
and the image display unit is used for displaying the insulator region to be detected, the confidence coefficient of the insulator region to be detected, the defect region of the insulator to be detected, the defect type and the confidence coefficient of the defect type in the insulator region to be detected with the defect.
Optionally, the method further includes:
the device comprises a first data set acquisition unit, a second data set acquisition unit and a third data set acquisition unit, wherein the first data set acquisition unit is used for acquiring a plurality of data sets containing images of insulators, and the insulator types in the data sets comprise ceramics, glass and composite insulators;
the first marking unit is used for marking the insulators in the data set;
the dividing unit is used for dividing the data set into a training set, a verification set and a test set;
and the first training unit is used for iteratively training the insulator positioning network to obtain the trained insulator positioning network.
Optionally, the method further includes:
the second data set acquisition unit is used for cutting the insulators in the images containing the insulator regions output by the insulator positioning network to form a training data set of the defect detection network;
the second labeling unit is used for labeling the defect position in the training data set;
and the second training unit is used for training the defect detection network to obtain the trained defect detection network.
Optionally, the method further includes:
the optimization unit is used for performing optimization calculation on the insulator positioning network or the insulator defect detection network by adopting an Adam algorithm;
according to the technical scheme, the method has the following advantages:
the application provides an insulator defect detection method, which comprises the steps of collecting an insulator image to be detected; inputting an insulator image to be detected into an insulator positioning network to obtain an insulator region to be detected in the insulator image to be detected and confidence coefficients of the insulator region to be detected; cutting the insulator region to be tested with the confidence coefficient larger than the confidence coefficient threshold value; and inputting the cut image of the insulator region to be detected into an insulator defect detection network, and identifying the defect region, the defect type and the confidence coefficient of the defect type of the insulator to be detected.
The method comprises an insulator positioning network and an insulator defect detection network, and the defect state is detected after the insulator in the image is detected. The insulator positioning network is responsible for detecting the insulators in the image, outputting position information and confidence coefficient information of the insulators, cutting out the insulators meeting the requirements according to the confidence coefficient, sending the cut insulator image to the insulator defect detection network for detecting the defect state, outputting the defect position information and the confidence coefficient state information of the insulators, displaying the defect position information and the confidence coefficient state information on the image, and realizing quick and accurate detection on common defects.
Drawings
FIG. 1 is a flowchart of a method according to an embodiment of a method for detecting insulator defects of the present application;
fig. 2 is a device configuration view of an embodiment of an insulator defect inspection device according to the present application;
fig. 3 is a schematic flowchart illustrating a process of identifying an insulator image according to an embodiment of the present application;
FIG. 4 is an image of an insulator image cropped according to an embodiment of the present application;
FIG. 5 is a schematic diagram of an output image of embodiment 1 after identifying an insulator defect according to the present application;
FIG. 6 is a schematic diagram of an output image of embodiment 2 after identifying an insulator defect according to the present application;
FIG. 7 is a schematic diagram of an output image of embodiment 3 after identifying an insulator defect according to the present application;
fig. 8 is a schematic diagram of an output image in embodiment 4 after identifying an insulator defect according to the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
Fig. 1 is a flowchart of a method according to an embodiment of a method for detecting an insulator defect of the present application, as shown in fig. 1, where fig. 1 includes:
101. collecting an insulator image to be detected;
it should be noted that, according to the method and the device, the insulator image on the power line can be acquired through aerial photography, and the image of the power transmission line at a high position which is difficult to identify is acquired.
102. Inputting an insulator image to be detected into an insulator positioning network to obtain an insulator region to be detected in the insulator image to be detected and confidence coefficients of the insulator region to be detected;
it should be noted that, the present application may adopt a cascade structure based on a YoloV3 deep learning network, as shown in fig. 3, fig. 3 includes an insulator positioning network and an insulator defect detection network. Due to different aerial photographing angles and distances, the proportion of the insulator in the collected insulator image may be small, and a general target detection model is usually insufficient in feature extraction on a small target, so that a cascade detection structure based on YOLOv3 is adopted in the application. As shown in fig. 3, after the aerial insulator image passes through the insulator positioning network, a rectangular frame containing the insulator and a confidence that the rectangular frame contains the insulator are output.
103. Cutting the insulator region to be tested with the confidence coefficient greater than the confidence coefficient threshold value;
it should be noted that, the method and the device can cut the insulator region to be detected, in which the confidence coefficient of the insulator included in the rectangular frame is greater than the confidence coefficient threshold, so as to obtain the rectangular frame image of the insulator region. The confidence coefficient threshold value can be set to be 0.6, namely the rectangular frame containing the insulator and having the confidence coefficient larger than or equal to 0.6 is cut.
Compared with the insulator image before cutting, the defect proportion of the insulator image is extremely small, cutting is equivalent to increasing the area of the defect, the problem of large noise interference caused by too small defect part can be avoided to a certain extent, so that defect information can be effectively extracted, the precision of subsequent defect detection is improved, and the model detection is facilitated. The cropped insulator picture is shown in fig. 4.
104. And inputting the cut image of the insulator region to be detected into an insulator defect detection network, and identifying the defect region, the defect type and the confidence coefficient of the defect type of the insulator to be detected.
It should be noted that, in the present application, the defect detection network may perform feature extraction, so as to effectively utilize the defect feature information. The insulator positioning network and the defect detection network predict the bounding box from the feature maps on three different scales, and finally the whole network outputs the position, category and confidence information of the insulator and the defects thereof. The rear end of the cascade structure is an insulator defect detection network, the rear end receives the cut insulator image, and the rear end structure is consistent with the front end to effectively extract insulator defect information.
Specifically, after the defect of the insulator is identified, the insulator sub-region to be detected, the confidence coefficient of the insulator sub-region to be detected, the defect region of the insulator to be detected, the defect type and the confidence coefficient of the defect type can be displayed in the insulator sub-image to be detected with the defect. Fig. 5 to 8 are schematic diagrams of output images after insulator defect identification, where the diagrams include a located insulator rectangular frame, a confidence level of a region including the insulator rectangular frame, an identified defect region, and a type and a confidence level of a defect to which the region belongs.
In a specific embodiment, the present application further comprises:
acquiring a plurality of data sets containing images of insulators, wherein the insulator types in the data sets comprise ceramics, glass and composite insulators;
marking insulators in the data set;
dividing a data set into a training set, a verification set and a test set;
iteratively training an insulator positioning network to obtain a trained insulator positioning network;
cutting insulators in the image containing the insulator region output by the insulator positioning network to form a training data set of the defect detection network;
marking the defect positions in the training data set;
and training the defect detection network to obtain the trained defect detection network.
It should be noted that, in the present application, insulator images including various defects may be selected as a data set, and the insulators in the data set mainly include three major types, i.e., ceramic, glass, and composite insulators. And then, carrying out image marking on the data set, marking the insulator region in the image, dividing the data set into a training set, a verification set and a test set according to the proportion of 8:1:1, and training the insulator positioning network, after the training of the insulator positioning network is finished, cutting the insulator region in the image, taking the cut image as the training set of the defect detection network, marking the defect part in the cut image, and training the defect detection network.
The method comprises an insulator positioning network and an insulator defect detection network, and the defect state is detected after the insulator in the image is detected. The insulator positioning network is responsible for detecting the insulators in the image, outputting position information and confidence coefficient information of the insulators, cutting out the insulators meeting the requirements according to the confidence coefficient, sending the cut insulator image to the insulator defect detection network for detecting the defect state, outputting the defect position information and the confidence coefficient state information of the insulators, displaying the defect position information and the confidence coefficient state information on the image, and realizing quick and accurate detection on common defects.
In a specific application example, the method adopts an Adam algorithm to perform optimization calculation on an insulator positioning network or an insulator defect detection network. The training iteration is 100 epochs (1 epoch is equal to one training using all samples in the training set, and the popular saying that the value of an epoch is the entire data set is trained several times), and the parameter setting is performed in stages. The weight attenuation regularization coefficient is 0.005, the impulse is 0.9, the batch size of the first 50 epochs is 8, the initial learning rate is 0.0001, and the learning rate attenuation coefficient is 0.9; the last 50 epochs have a batch _ size of 4, an initial learning rate of 0.00001, and a learning rate decay factor of 0.9, at which stage the model parameters are mainly trimmed. The experiment adopts a cross iterative training mode, one epoch verification set is verified every time an epoch training set is passed, so that the parameters of the model can be adjusted in time, and then the model state of each epoch is stored, including the training and verification loss values of each epoch and the like.
TABLE 1 Experimental parameter configuration
For the measurement of precision, the precision, recall, AP and F values are mainly adopted for quantitative analysis (when the number of detected categories is 1, AP and mAP are equal in value), and the higher the corresponding numerical value is, the better the detection performance of the model is, and the higher the detection accuracy is. The IoU (cross-over ratio) threshold set in the verification experiment of the application is 0.5 (the higher the cross-over ratio is set, the higher the requirement on the accuracy of the network detection performance is). As can be seen from table 2 and the following fig. 2, the cascade model has good performance of insulator sub-target identification, insulator spontaneous explosion, and insulator discharge defect detection.
TABLE 2 Cascade detection model test results
The visual detection effect of the model under various types of insulators and various backgrounds is shown in figures 5-8. It can be seen that the YOLOv3 cascade detection structure can have relatively accurate detection effects on insulators, insulator spontaneous explosion, insulator discharge and the like. The detection effect also corresponds to the value of the detection evaluation index value, which shows that the YOLOv3 cascade defect detection structure provided by the application can effectively detect defects, the generalization capability and the robustness of the model are strong, and the accurate positioning and detection and identification of the insulator and the defects thereof are realized.
In one specific embodiment, 1726 data sets for detecting defects are obtained from 10kv low-voltage power lines, and the image size is the same as that of a normal insulator and is named as "defect _ insulator". Two common insulator defects are selected in the experiment of the application: the defect detection experiment was performed by auto-detonation (missing caps) and discharge (lightning). The images are named according to the format of "name _ number" and the details are shown in table 3 below.
TABLE 3 data set information
In a power transmission line, high-voltage pins, strain suspension insulators, low-voltage shackles and cable insulators are used for medium and high voltage, high-voltage line insulators are slender, low-voltage line insulators are different in shape, such as column type, rod type and basin type, and data concentration insulators mainly comprise three types, namely ceramic, glass and composite insulators. The method comprises the steps of dividing the model into a training set, a verification set and a test set according to the ratio of 8:1:1, wherein in machine learning, the training set is used for training model parameters, the test set is used for testing the generalization ability of the model, and the verification set is used as feedback of the training performance of the model in the model training process and is used for selecting hyper-parameters of the model and judging whether over-fitting or under-fitting exists.
The data set used for model training in the application adopts a PASCALVOC2007 data set format and mainly comprises an image file, an annotation file and an index file. And image annotation is carried out by adopting LabelImg open source data annotation software.
Next, model training performed on the server configured with the GPU is performed, and other hardware information and software information, such as the Ubuntu system, the cpu, and the memory, of the server operating system are as shown in table 4 below:
table 4 experimental configuration information
The whole defect cascade detection structure follows the modes of respectively independent training and joint structure prediction, namely, an insulator positioning network is trained to position insulators in images, and a defect detection network is trained to identify defects in the insulators. And (3) positioning the insulators on the 'defect _ insulator' data set, and cutting corresponding insulators according to a network output result to form a training data set of the defect detection network. And marking the cut defective insulator images which meet the standard (threshold range) in accordance with the training mode of the insulator positioning network, sending the images into a detection network to generate optimized network parameters, and finally obtaining the defect detection network with strong robustness.
When network prediction is carried out, firstly, insulator positioning is carried out on an input image through an insulator positioning network, then, corresponding insulators are cut according to confidence coefficient values, then, the cut insulator image is sent to a defect detection network to carry out defect identification classification and positioning, and finally, the position, the confidence coefficient, the category information and the like of the insulators and the defects are output on the same image.
The present application further provides an embodiment of an insulator defect detecting apparatus, as shown in fig. 2, where fig. 2 includes:
the acquisition unit 201 is used for acquiring an image of the insulator to be detected;
the insulator positioning unit 202 is configured to input the insulator image to be detected to an insulator positioning network, so as to obtain an insulator area to be detected in the insulator image to be detected and a confidence coefficient of the insulator area to be detected;
the cutting unit 203 is used for cutting the insulator region to be measured with the confidence coefficient greater than the confidence coefficient threshold value;
and the defect identification unit 204 is configured to input the cut image of the insulator region to be detected into the insulator defect detection network, and identify the defect region of the insulator to be detected, the defect type, and the confidence of the defect type.
In a specific embodiment, the method further comprises the following steps:
and the image display unit is used for displaying the insulator region to be detected, the confidence coefficient of the insulator region to be detected, the defect region of the insulator to be detected, the defect type and the confidence coefficient of the defect type in the insulator region to be detected with the defect.
In a specific embodiment, the method further comprises the following steps:
the first data set acquisition unit is used for acquiring a plurality of data sets containing images of the insulators, and the insulator types in the data sets comprise ceramics, glass and composite insulators;
the first marking unit is used for marking the insulators in the data set;
the dividing unit is used for dividing the data set into a training set, a verification set and a test set;
and the first training unit is used for iteratively training the insulator positioning network to obtain the trained insulator positioning network.
In a specific embodiment, the method further comprises the following steps:
the second data set acquisition unit is used for cutting the insulators in the images containing the insulator regions output by the insulator positioning network to form a training data set of the defect detection network;
the second labeling unit is used for labeling the defect position in the training data set;
and the second training unit is used for training the defect detection network to obtain the trained defect detection network.
In a specific embodiment, the method further comprises the following steps:
and the optimization unit is used for performing optimization calculation on the insulator positioning network or the insulator defect detection network by adopting an Adam algorithm.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, 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.
It should be understood that in the present application, "at least one" means one or more, "a plurality" means two or more. "and/or" for describing an association relationship of associated objects, indicating that there may be three relationships, e.g., "a and/or B" may indicate: only A, only B and both A and B are present, wherein A and B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of single item(s) or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. An insulator defect detection method is characterized by comprising the following steps:
collecting an insulator image to be detected;
inputting the insulator image to be detected into an insulator positioning network to obtain an insulator region to be detected in the insulator image to be detected and the confidence coefficient of the insulator region to be detected;
cutting the insulator region to be tested with the confidence coefficient larger than the confidence coefficient threshold value;
and inputting the cut image of the insulator region to be detected into an insulator defect detection network, and identifying the defect region, the defect type and the confidence coefficient of the defect type of the insulator to be detected.
2. The insulator defect detection method according to claim 1, further comprising:
and displaying the insulator region to be detected, the confidence coefficient of the insulator region to be detected, the defect region of the insulator to be detected, the defect type and the confidence coefficient of the defect type in the insulator region to be detected with the defect.
3. The insulator defect detection method according to claim 1, further comprising: before inputting the insulator image to be detected into an insulator positioning network to obtain the insulator region to be detected in the insulator image to be detected and the confidence coefficient of the insulator region to be detected, the method further comprises the following steps:
acquiring a plurality of data sets containing images of insulators, wherein the insulator types in the data sets comprise ceramics, glass and composite insulators;
marking the insulators in the data set;
dividing the data set into a training set, a verification set and a test set;
and iteratively training the insulator positioning network to obtain the trained insulator positioning network.
4. The insulator defect detection method according to claim 3, further comprising: before the step of inputting the cut image of the insulator region to be detected into the insulator defect detection network, the method further comprises the following steps:
cutting the insulators in the image containing the insulator region output by the insulator positioning network to form a training data set of the defect detection network;
marking the defect positions in the training data set;
and training the defect detection network to obtain the trained defect detection network.
5. The insulator defect detection method according to claim 4, wherein an Adam algorithm is adopted to perform optimization calculation on the insulator positioning network or the insulator defect detection network.
6. An insulator defect detecting device, comprising:
the acquisition unit is used for acquiring an image of the insulator to be detected;
the insulator positioning unit is used for inputting the insulator image to be detected into an insulator positioning network to obtain an insulator area to be detected in the insulator image to be detected and the confidence coefficient of the insulator area to be detected;
the cutting unit is used for cutting the insulator region to be detected with the confidence coefficient larger than the confidence coefficient threshold value;
and the defect identification unit is used for inputting the cut image of the insulator region to be detected into the insulator defect detection network and identifying the defect region, the defect type and the confidence coefficient of the defect type of the insulator to be detected.
7. The insulator defect detecting apparatus according to claim 6, further comprising:
and the image display unit is used for displaying the insulator region to be detected, the confidence coefficient of the insulator region to be detected, the defect region of the insulator to be detected, the defect type and the confidence coefficient of the defect type in the insulator region to be detected with the defect.
8. The insulator defect detecting apparatus according to claim 6, further comprising:
the device comprises a first data set acquisition unit, a second data set acquisition unit and a third data set acquisition unit, wherein the first data set acquisition unit is used for acquiring a plurality of data sets containing images of insulators, and the insulator types in the data sets comprise ceramics, glass and composite insulators;
the first marking unit is used for marking the insulators in the data set;
the dividing unit is used for dividing the data set into a training set, a verification set and a test set;
and the first training unit is used for iteratively training the insulator positioning network to obtain the trained insulator positioning network.
9. The insulator defect detecting apparatus according to claim 8, further comprising:
the second data set acquisition unit is used for cutting the insulators in the images containing the insulator regions output by the insulator positioning network to form a training data set of the defect detection network;
the second labeling unit is used for labeling the defect position in the training data set;
and the second training unit is used for training the defect detection network to obtain the trained defect detection network.
10. The insulator defect detecting apparatus according to claim 9, further comprising:
and the optimization unit is used for performing optimization calculation on the insulator positioning network or the insulator defect detection network by adopting an Adam algorithm.
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