WO2022036919A1 - 缺陷检测方法、装置、电子设备及计算机存储介质 - Google Patents
缺陷检测方法、装置、电子设备及计算机存储介质 Download PDFInfo
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Definitions
- the present application relates to the field of computer vision technology, and in particular, to a defect detection method, apparatus, electronic device, and computer storage medium.
- the present application provides a defect detection method, device, electronic device and storage medium, which are beneficial to reduce the missed detection rate of high-speed rail catenary defect detection and improve the accuracy of catenary defect detection.
- a defect detection method the method includes:
- Defect classification is performed on the first part and the second part based on the image of the first part and the image of the second part, and a defect classification result is obtained.
- the segmentation of the image of the first component of the high-speed rail contact net from the to-be-detected image includes:
- the image of the first component is segmented from the to-be-detected image according to the first rectangular detection frame.
- the positioning and classification of the first component based on the first feature map to obtain a first rectangular detection frame of the first component includes:
- the first part is classified based on the first pooling feature to obtain the category of the first part and the first rectangular detection frame.
- the segmentation of the image of the second component of the high-speed rail contact net from the image of the first component includes:
- the image of the second part is segmented from the image to be segmented of the first part.
- segmenting the image of the second component from the to-be-segmented image of the first component includes:
- the image of the second part is segmented from the image to be segmented of the first part according to the second rectangular detection frame.
- the positioning and classification of the second component based on the second feature map to obtain a second rectangular detection frame of the second component includes:
- the defect classification is performed on the first part and the second part based on the image of the first part and the image of the second part to obtain defects Classification results, including:
- the defect classification result of the first part includes the defect classification result of the first part. the type of defect and the category of the first part;
- the defect classification result of the second part includes the defect classification result of the second part. Defect type and category of the second part.
- defect classification is performed on the first part and the second part based on the image of the first part and the image of the second part to obtain a defect classification
- the method further includes:
- Defect early warning is performed according to the defect classification result.
- the performing a defect early warning according to the defect classification result includes:
- For the first part output the defect type of the first part, the position of the first part in the image to be inspected, the type of the first part, and the high-speed rail line where the first part is located, for defect warning;
- For the second part determine the target first part to which the second part belongs, and output the defect type of the second part, the position of the second part in the image to be inspected, the second part The category of the target first part, the position of the target first part in the to-be-detected image, the category of the target first part and the high-speed rail line where the second part is located, so as to carry out defect early warning.
- the obtaining of the image to be detected of the high-speed rail catenary includes:
- the original image of the high-speed rail catenary is filtered to obtain the to-be-detected image.
- a second aspect of the embodiments of the present application provides a defect detection device, the device comprising:
- the image acquisition module is used to acquire the image to be detected of the high-speed rail catenary
- a first detection module used for segmenting the image of the first component of the high-speed rail catenary from the to-be-detected image
- a second detection module configured to segment the image of the second part of the high-speed rail contact net from the image of the first part; the second part is a sub-component of the first part;
- the defect classification module is configured to perform defect classification on the first part and the second part based on the image of the first part and the image of the second part, and obtain a defect classification result.
- a third aspect of the embodiments of the present application provides an electronic device, the electronic device includes an input device and an output device, and further includes a processor, adapted to implement one or more instructions; and, a computer storage medium, the computer storage medium storing There is one or more instructions adapted to be loaded by the processor and to perform the steps in any of the embodiments of the first aspect above.
- a fourth aspect of the embodiments of the present application provides a computer storage medium, where the computer storage medium stores one or more instructions, and the one or more instructions are suitable for being loaded by a processor and executing any one of the foregoing first aspects steps in the implementation.
- the embodiment of the present application obtains the image to be detected of the high-speed rail catenary; the image of the first part of the high-speed rail catenary is segmented from the to-be-detected image; the high-speed rail contact is segmented from the image of the first part an image of a second part of the web; the second part is a sub-part of the first part; based on the image of the first part, the image of the second part Parts are classified into defects, and the result of defect classification is obtained.
- the first-level component ie, the first component
- the image of the first-level component is segmented from the to-be-detected image of the high-speed railway contact line
- the image of the first-level component is processed for the second-level component. (that is, the second part)
- identify the secondary part on the primary part segment the image of the secondary part
- the high-speed rail catenary defect detection is beneficial to reduce the missed detection rate of high-speed rail catenary defect detection, thereby improving the accuracy of catenary defect detection.
- FIG. 1 is a schematic flowchart of a defect detection method provided by an embodiment of the present application.
- FIG. 2 is a schematic diagram of an application environment for defect detection of a high-speed rail catenary provided by an embodiment of the present application
- FIG. 3 is a schematic diagram of filtering an original image of a high-speed rail catenary according to an embodiment of the present application
- FIG. 4 is a schematic structural diagram of a defect detection model for a high-speed rail catenary provided by an embodiment of the present application
- 5A is a schematic diagram of dividing a first component according to an embodiment of the present application.
- 5B is a schematic diagram of dividing a second component according to an embodiment of the present application.
- FIG. 6 is a schematic flowchart of another defect detection method provided by an embodiment of the present application.
- FIG. 7 is a schematic diagram of generating a candidate region based on a feature map according to an embodiment of the application.
- FIG. 8 is a schematic structural diagram of a defect detection device provided by an embodiment of the application.
- FIG. 9 is a schematic structural diagram of another defect detection device provided by an embodiment of the application.
- FIG. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
- the embodiment of the present application proposes a defect detection scheme for a high-speed rail catenary, so as to reduce the missed detection rate of defect detection of the high-speed rail catenary and improve the accuracy of defect detection.
- a high-speed rail catenary defect detection model based on deep learning is adopted. First, the first-level components are located from the images of the high-speed rail catenary to be inspected, and then the first-level components are located from the images of the first-level components. The second-level components of the relationship are beneficial to reduce the missed detection rate of the components.
- the images of the first-level components and the images of the second-level components are used to predict the defect types of the first-level components and the second-level components, and the specific location of the defect can be output in the final defect warning.
- defect type the component to which the defect belongs, the superior component of the component to which the defect belongs, and the specific line segment where the defect is located. , so as to carry out the maintenance of the catenary to ensure the safe operation of the high-speed rail, and at the same time, it is conducive to the expansion of new components and new defects.
- FIG. 1 is a schematic flowchart of a defect detection method provided by an embodiment of the application.
- the defect detection method is applied to a server, such as a server and a computer host where a deep learning-based high-speed rail catenary defect detection model is deployed. , cloud server, etc., as shown in Figure 1, including steps S11-S14:
- the high-speed rail inspection vehicle usually operates at night.
- the inspection vehicle is equipped with high-definition imaging equipment and on-board sensors.
- the inspection vehicle travels on the high-speed rail line.
- the on-board sensor detects
- the imaging device is triggered to collect images of the catenary, and the original image of the high-speed rail catenary is obtained.
- two sets of imaging equipment are triggered to image the front and back and overall layout of the support parts and suspension parts of the catenary, thus obtaining a large number of original images of the high-speed rail catenary from different angles.
- the resolution of the original image of the high-speed rail catenary usually has a better value, for example: 6576*4384 pixels, but due to environmental factors such as night operations and foggy, the collected original images of the high-speed rail catenary still have low resolution.
- the resolution length and width are less than 2000 pixels. Therefore, as shown in Figure 3, it is necessary to filter the collected original images of the high-speed rail catenary, and filter out the high-speed rail catenary whose resolution length and width reach the preset pixel value.
- the original image is used as the image to be detected for subsequent defect detection, and the original image of the high-speed rail catenary whose resolution length and width are lower than the preset pixel value is filtered out.
- S12 segment the image of the first component of the high-speed rail catenary from the to-be-detected image.
- a pre-trained deep learning-based high-speed rail catenary defect detection model is used to perform defect detection on each component in the to-be-detected image obtained in step S11, and the high-speed rail catenary defect detection model includes a first component detection model.
- the input of the first part detector is the image to be detected, which is used to detect the first part of the high-speed rail catenary from the image to be detected , such as: column top cover plate, insulator, ring rod-right angle hanging plate joint, arm wrist base, AF wire shoulder frame base, contact wire center anchor clamp, drop weight limit frame, etc.
- the second part detector is used to The second part on the first part is detected in the image of the first part output by the first part detector, such as bolts, nuts, cotter pins, etc.
- the defect classifier is used for The first part is classified according to the image of the first part, and the second part is classified according to the image of the second part; The location, the type of defect (such as the angle of the cotter pin on the arm base is not in place), the superior component of the component to which the defect belongs, the high-speed rail line where the defect is located, etc.
- the first component detector may be a two-stage detector or a one-stage detector. The two-stage detector generates candidate regions based on the feature maps extracted from the images to be detected, and then analyzes the candidate regions. Perform classification prediction to obtain the category of the first component and the coordinates of the rectangular detection frame.
- the coordinates of the rectangular detection frame can be the coordinates of the upper left corner and the lower right corner, or the coordinates of the center point and length and width, etc., which are not limited in detail, as shown in Figure 5A
- the image of the first component such as the insulator, the arm-wrist base, etc.
- the one-stage detector does not need to generate a candidate area, it directly performs classification prediction for the input image to be detected, obtains the category of the first part and the coordinates of the rectangular detection frame, and then divides the image of the first part according to the rectangular detection frame.
- the first component detector is trained using a sample image of the high-speed rail catenary, the first component in the sample image has a class label, and the first component detector is adjusted by a preset loss function during the training process. excellent.
- the second component refers to the sub-component on the first component, and the two are in a cascade relationship.
- the image of the first part segmented in step S12 is detected, and the possibility of missed detection is high. Therefore, it is necessary to perform gamma check on the image of the first part to improve the image quality and obtain the to-be-segmented image of the first part. image (that is, the image obtained after gamma verification), and then segment the image of the second component of the high-speed rail catenary from the image to be segmented by the second component detector.
- the second part detector can be the same as the first part detector, or it can be different, it can be trained together with the first part detector, or can be trained separately, in the same way, the category and rectangle of the second part can be obtained.
- the image of the second part is segmented from the image of the first part according to the rectangular detection frame.
- S14 Perform defect classification on the first part and the second part based on the image of the first part and the image of the second part, to obtain a defect classification result.
- the above-mentioned performing defect classification on the first part and the second part based on the image of the first part and the image of the second part, to obtain a defect classification result including:
- the defect classification result of the first part includes the defect classification result of the first part. the type of defect and the category of the first part;
- the defect classification result of the second part includes the defect classification result of the second part. Defect type and category of the second part.
- the defect classifier After obtaining the image of the first part and the image of the second part, they are input into the defect classifier for probability prediction of defect types. Specifically, the feature of the image of the first part and the feature of the image of the second part are extracted through the backbone network of the defect classifier.
- the backbone network mainly performs convolution processing, and then based on the extracted features, the features are input into the fully connected layer Predict the probability of defect types, and take the defect type with the highest probability as the defect type of the part. For example, the characteristics of the first part of the pendulum limit frame are currently input, and the fully connected layer is classified to predict the existence of cracks in the pendulum head restraint frame.
- the defect type of the pendulum limit frame is the existence of cracks.
- the final output of the defect classifier also has the class index of the part and the coordinates of the rectangular detection frame, for example: c05, if there is a crack, c05 means the class index of the part, among which, the class index of the first part
- the class index of the second part can be determined when the class of the first part is obtained by the first part detector, and the class index of the second part can be determined when the class of the second part is obtained by the second part detector.
- the defect classifier can be trained together with the first part detector, can also be trained together with the second part detector, or can be trained separately.
- the method further includes: according to The defect classification result is used for defect early warning.
- the performing defect early warning according to the defect classification result includes:
- For the first part output the defect type of the first part, the position of the first part in the image to be inspected, the type of the first part, and the high-speed rail line where the first part is located, for defect warning;
- For the second part determine the target first part to which the second part belongs, and output the defect type of the second part, the position of the second part in the image to be inspected, the second part The category of the target first part, the position of the target first part in the to-be-detected image, the category of the target first part and the high-speed rail line where the second part is located, so as to carry out defect early warning.
- the target first part refers to the upper-level part of the second part.
- the input of the defect warning module includes the defect classification results of the first part and the second part, the coordinates of the rectangular detection frame of the first part and the second part, and the output part The type of defect, the location of the component in the image to be inspected, the category of the component, the location of the superior component to which the component belongs in the image to be inspected, the category of the superior component of the component, and the high-speed rail line where the component is located.
- the position of the component in the image to be detected and the position of the upper-level component of the component in the image to be detected are presented in the rectangular detection frame of the component, the category of the component and the category of the parent component of the component are presented with the category index, the component
- the high-speed rail line you are in can output according to the logo carried when the imaging device uploads the original image of the high-speed railway catenary. It will exist in the entire defect detection process. Of course, this is only an example, and does not limit the embodiments of the present application.
- the early warning information output by the first part includes the defect type of the first part (for example, the nut on the AF wire shoulder frame has fallen off), the defect of the first part in the image to be detected. Position (such as the rectangular detection frame of the AF line shoulder frame base), the type of the first part, and the high-speed rail line where the first part is located.
- the second part usually stores the upper-level part. Therefore, it is necessary to determine the target first part to which the second part belongs. When outputting information such as the defect type of the second part itself, the position of the second part in the image to be inspected, etc., it is also necessary to output the The category of the first part of the target, the position in the image to be detected, etc.
- the determining of the target first component to which the second component belongs includes:
- the target first part to which the second part belongs is determined according to the ratio between the second rectangular detection frames.
- the first rectangular detection frame refers to the bounding box regression of the first component
- the second rectangular detection frame refers to the bounding box regression of the second component.
- the defect early warning module inputs the defect classification results of the first part and the second part, and the coordinates of the rectangular detection frame of the first part and the second part
- the rectangular detection frame coordinates of the first part and the second part can be The ratio of the intersection of the detection frame and the rectangular detection frame of the second component is used to determine the upper-level component of the second component, which does not affect the overall detection time, and the accuracy meets the requirements.
- the image to be detected of the high-speed rail catenary is obtained; the image of the first part of the high-speed rail catenary is segmented from the to-be-detected image; the second part of the high-speed rail catenary is segmented from the image of the first part an image of a part; the second part is a sub-part of the first part; the first part and the second part are classified as defective based on the image of the first part and the image of the second part , get the defect classification result.
- the first-level component is detected on the image to be detected of the high-speed rail contact line
- the image of the first-level component is segmented from the to-be-detected image of the high-speed rail contact line
- the second level component is detected on the image of the first-level component to identify
- the secondary part on the primary part, the image of the secondary part is segmented, and the image of the primary part and the image of the secondary part are used for defect classification, which realizes the cascading high-speed rail catenary defect detection, which is beneficial to reduce the high-speed rail.
- the missed detection rate of catenary defect detection thereby improving the accuracy of catenary defect detection.
- it is also beneficial to reduce the cost of manual inspection, shorten the detection time, and improve the detection efficiency.
- FIG. 6 is a schematic flowchart of another defect detection method provided by an embodiment of the present application, as shown in FIG. 6, including steps S61-S67:
- the backbone network mainly performs convolution processing, and the output feature map is the above-mentioned feature map.
- the first feature map, the coordinates of the candidate region of the first component are predicted on the first feature map, and the candidate region shown in Figure 7 is generated, and the front and background classification is performed in the candidate region to obtain the foreground target of the first component.
- the corresponding features of the foreground target in the first feature map are pooled, and the output features are the above-mentioned first pooling features, the first pooling features are input into the fully connected layer for final classification, and the output
- the category of the first part in the image to be inspected and the rectangular inspection frame ie, the first rectangular inspection frame
- the image of the first part is segmented from the image to be inspected according to the first rectangular inspection frame, as the input of the defect classifier.
- the first part detector based on the candidate region is used to classify the first part in the image to be detected, and the accuracy is higher.
- the segmentation of the image of the second component of the high-speed rail catenary from the image of the first component includes:
- the image of the second part is segmented from the image to be segmented of the first part.
- performing gamma verification on the image of the first component can obtain an image to be segmented with better quality, which is beneficial to overcome the influence of poor light on detection, so as to accurately segment the image of the second component, reduce The miss rate of the second part.
- segmenting the image of the second part from the image to be segmented of the first part includes:
- the image of the second part is segmented from the image to be segmented of the first part according to the second rectangular detection frame.
- the positioning and classification of the second component based on the second feature map to obtain a second rectangular detection frame of the second component includes:
- the second feature map refers to the feature map extracted by the second component detector from the sub-graph of the first component through the backbone network
- the second pooling feature refers to the second component detector's effect on the second component.
- the processing procedure of the second component detector is the same as that of the first component detector. It also predicts the category of the second component based on the generated candidate area, and outputs the category of the second component and the second rectangular detection frame.
- FIG. 8 is a schematic structural diagram of a defect detection apparatus provided by an embodiment of the present application. As shown in Figure 8, the device includes:
- the image acquisition module 81 is used to acquire the to-be-detected image of the high-speed rail catenary
- the first detection module 82 is used for segmenting the image of the first component of the high-speed rail catenary from the to-be-detected image;
- the second detection module 83 is configured to segment the image of the second part of the high-speed rail contact net from the image of the first part; the second part is a sub-component of the first part;
- the defect classification module 84 is configured to perform defect classification on the first part and the second part based on the image of the first part and the image of the second part to obtain a defect classification result.
- the first detection module 82 is specifically configured to:
- the image of the first component is segmented from the to-be-detected image according to the first rectangular detection frame.
- the first detection module 82 is specifically configured to :
- the first part is classified based on the first pooling feature to obtain the category of the first part and the first rectangular detection frame.
- the second detection module 83 is specifically configured to:
- the image of the second part is segmented from the image to be segmented of the first part.
- the second detection module 83 is specifically configured to:
- the image of the second part is segmented from the image to be segmented of the first part according to the second rectangular detection frame.
- the second detection module 83 is specifically configured to:
- the defect classification Module 84 is specifically used to:
- the defect classification result of the first part includes the defect classification result of the first part. the type of defect and the category of the first part;
- the defect classification result of the second part includes the defect classification result of the second part. Defect type and category of the second part.
- the device further includes a defect early warning module 85; the defect early warning module 85 is specifically used for:
- Defect early warning is performed according to the defect classification result.
- the defect early warning module 85 is specifically used for:
- For the first part output the defect type of the first part, the position of the first part in the image to be inspected, the type of the first part, and the high-speed rail line where the first part is located, for defect warning;
- For the second part determine the target first part to which the second part belongs, and output the defect type of the second part, the position of the second part in the image to be inspected, the second part The category of the target first part, the position of the target first part in the to-be-detected image, the category of the target first part and the high-speed rail line where the second part is located, so as to carry out defect early warning.
- the image acquisition module 81 is specifically used for:
- the original image of the high-speed rail catenary is filtered to obtain the to-be-detected image.
- each unit in the defect detection apparatus shown in FIG. 8 or FIG. 9 may be respectively or all merged into one or several other units to form, or some unit(s) may also be It is further divided into multiple units with smaller functions, which can realize the same operation without affecting the realization of the technical effects of the embodiments of the present application.
- the above-mentioned units are divided based on logical functions.
- the function of one unit may also be implemented by multiple units, or the functions of multiple units may be implemented by one unit.
- the defect-based detection device may also include other units, and in practical applications, these functions may also be implemented with the assistance of other units, and may be implemented by cooperation of multiple units.
- a general-purpose computing device such as a computer
- a general-purpose computing device may be implemented on a general-purpose computing device including a central processing unit (CPU), a random access storage medium (RAM), a read-only storage medium (ROM), and other processing elements and storage elements.
- CPU central processing unit
- RAM random access storage medium
- ROM read-only storage medium
- Running a computer program capable of executing the steps involved in the corresponding method as shown in FIG. 1 or FIG. 6, to construct the defect detection apparatus as shown in FIG. 8 or FIG. 9, and to realize the present invention.
- the defect detection method of the application embodiment is provided.
- the computer program can be recorded on, for example, a computer-readable recording medium, and loaded in the above-mentioned computing device through the computer-readable recording medium, and executed therein.
- the embodiments of the present application further provide an electronic device.
- the electronic device includes at least a processor 1001 , an input device 1002 , an output device 1003 and a computer storage medium 1004 .
- the processor 1001 , the input device 1002 , the output device 1003 and the computer storage medium 1004 in the electronic device may be connected through a bus or other means.
- the computer storage medium 1004 can be stored in the memory of the electronic device, the computer storage medium 1004 is used for storing a computer program, the computer program includes program instructions, and the processor 1001 is used for executing the program stored in the computer storage medium 1004 instruction.
- the processor 1001 (or called CPU (Central Processing Unit, central processing unit)) is the computing core and the control core of the electronic device, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve Corresponding method flow or corresponding function.
- CPU Central Processing Unit, central processing unit
- the processor 1001 of the electronic device provided in this embodiment of the present application may be configured to perform a series of defect detection processes:
- Defect classification is performed on the first part and the second part based on the image of the first part and the image of the second part, and a defect classification result is obtained.
- the processor 1001 executes the segmentation of the image of the first component of the high-speed rail catenary from the to-be-detected image, including:
- the image of the first component is segmented from the to-be-detected image according to the first rectangular detection frame.
- the processor 1001 executes the positioning and classification of the first component based on the first feature map to obtain a first rectangular detection frame of the first component, including:
- the first part is classified based on the first pooling feature to obtain the category of the first part and the first rectangular detection frame.
- the processor 1001 executes the segmentation of the image of the second component of the high-speed rail catenary from the image of the first component, including:
- the image of the second part is segmented from the image to be segmented of the first part.
- the processor 1001 performing the segmenting of the image of the second part from the image to be segmented of the first part includes:
- the image of the second part is segmented from the image to be segmented of the first part according to the second rectangular detection frame.
- the processor 1001 executes the positioning and classification of the second component based on the second feature map to obtain a second rectangular detection frame of the second component, including:
- the processor 1001 performs the defect classification on the first part and the second part based on the image of the first part and the image of the second part, to obtain a defect classification result, including: :
- the defect classification result of the first part includes the defect classification result of the first part. the type of defect and the category of the first part;
- the defect classification result of the second part includes the defect classification result of the second part. Defect type and category of the second part.
- the processor 1001 further uses To execute:
- Defect early warning is performed according to the defect classification result.
- the processor 1001 executes the defect early warning according to the defect classification result, including:
- For the first part output the defect type of the first part, the position of the first part in the image to be inspected, the type of the first part, and the high-speed rail line where the first part is located, for defect warning;
- For the second part determine the target first part to which the second part belongs, and output the defect type of the second part, the position of the second part in the image to be inspected, the second part The category of the target first part, the position of the target first part in the to-be-detected image, the category of the target first part and the high-speed rail line where the second part is located, so as to carry out defect early warning.
- the processor 1001 executes the obtaining of the image to be detected of the high-speed rail catenary, including:
- the original image of the high-speed rail catenary is filtered to obtain the to-be-detected image.
- the above-mentioned electronic device may be a computer, a computer host, a server, a cloud server, a server cluster, etc.
- the electronic device may include but is not limited to a processor 1001, an input device 1002, an output device 1003, and a computer storage medium 1004.
- the input device 1002 It can be a keyboard, a touch screen, etc.
- the output device 1003 can be a speaker, a display, a radio frequency transmitter, and the like.
- the schematic diagram is only an example of an electronic device, and does not constitute a limitation to the electronic device, and may include more or less components than the one shown, or combine some components, or different components.
- the processor 1001 of the electronic device implements the steps in the above-mentioned defect detection method when executing the computer program, the embodiments of the above-mentioned defect detection method are all applicable to the electronic device, and can achieve the same or similar benefits. Effect.
- Embodiments of the present application further provide a computer storage medium (Memory), where the computer storage medium is a memory device in an electronic device and is used to store programs and data.
- the computer storage medium here may include both a built-in storage medium in the terminal, and certainly also an extended storage medium supported by the terminal.
- the computer storage medium provides storage space, and the storage space stores the operating system of the terminal.
- one or more instructions suitable for being loaded and executed by the processor 1001 are also stored in the storage space, and these instructions may be one or more computer programs (including program codes).
- the computer storage medium here can be a high-speed RAM memory, or a non-volatile memory (non-volatile memory), such as at least one disk memory; optionally, it can also be at least one memory located far away from the aforementioned processing
- the computer storage medium of the device 1001 can be loaded and executed by the processor 1001 to implement the corresponding steps of the above-mentioned defect detection method.
- the computer program of the computer storage medium includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, and the like.
- the computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, removable hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.
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Abstract
Description
Claims (22)
- 一种缺陷检测方法,其特征在于,所述方法包括:获取高铁接触网的待检测图像;从所述待检测图像中分割出高铁接触网的第一部件的图像;从所述第一部件的图像中分割出高铁接触网的第二部件的图像;所述第二部件为所述第一部件的子部件;基于所述第一部件的图像、所述第二部件的图像对所述第一部件、所述第二部件进行缺陷分类,得到缺陷分类结果。
- 根据权利要求1所述的方法,其特征在于,所述从所述待检测图像中分割出高铁接触网的第一部件的图像,包括:对所述待检测图像进行特征提取,得到第一特征图;基于所述第一特征图对所述第一部件进行定位和分类,得到所述第一部件的第一矩形检测框;按照所述第一矩形检测框从所述待检测图像中分割出所述第一部件的图像。
- 根据权利要求2所述的方法,其特征在于,所述基于所述第一特征图对所述第一部件进行定位和分类,得到所述第一部件的第一矩形检测框,包括:在所述第一特征图上进行所述第一部件的候选区域坐标预测和前、背景分类,确定出所述第一部件的前景目标;将所述第一部件的前景目标在所述第一特征图中对应的特征进行池化处理,得到第一池化特征;基于所述第一池化特征对所述第一部件进行分类,得到所述第一部件的类别和所述第一矩形检测框。
- 根据权利要求3所述的方法,其特征在于,所述从所述第一部件的图像中分割出高铁接触网的第二部件的图像,包括:对所述第一部件的图像进行伽马校验,得到所述第一部件的待分割图像;从所述第一部件的待分割图像中分割出所述第二部件的图像。
- 根据权利要求4所述的方法,其特征在于,所述从所述第一部件的待分割图像中分割出所述第二部件的图像,包括:对所述第一部件的待分割图像进行特征提取,得到第二特征图;基于所述第二特征图对所述第二部件进行定位和分类,得到所述第二部件的第二矩形检测框;按照所述第二矩形检测框从所述第一部件的待分割图像中分割出所述第二部件的图像。
- 根据权利要求5所述的方法,其特征在于,所述基于所述第二特征图对所述第二部件进行定位和分类,得到所述第二部件的第二矩形检测框,包括:在所述第二特征图上进行所述第二部件的候选区域坐标预测和前、背景分类,确定出所述第二部件的前景目标;将所述第二部件的前景目标在所述第二特征图中对应的特征进行池化处理,得到第二池化特征;基于所述第二池化特征对所述第二部件进行分类,得到所述第二部件的类别和所述第二矩形检测框。
- 根据权利要求6所述的方法,其特征在于,所述基于所述第一部件的图像、所述第二部件的图像对所述第一部件、所述第二部件进行缺陷分类,得到缺陷分类结果,包括:分别对所述第一部件的图像、所述第二部件的图像进行特征提取;基于所述第一部件的图像提取出的特征对所述第一部件进行缺陷类型预测,得到所述第一部件的缺陷分类结果;所述第一部件的缺陷分类结果包括所述第一部件的缺陷类型和所述第一部件的类别;以及,基于所述第二部件的图像提取出的特征对所述第二部件进行缺陷类型预测,得到所述第二部件的缺陷分类结果;所述第二部件的缺陷分类结果包括所述第二部件的缺陷类型和所述第二部件的类别。
- 根据权利要求7所述的方法,其特征在于,在基于所述第一部件的图像、所述第二部件的图像对所述第一部件、所述第二部件进行缺陷分类,得到缺陷分类结果之后,所述方法还包括:根据所述缺陷分类结果进行缺陷预警。
- 根据权利要求8所述的方法,其特征在于,所述根据所述缺陷分类结果进行缺陷预警,包括:针对所述第一部件,输出所述第一部件的缺陷类型、所述第一部件在所述待检测图像中的位置、所述第一部件的类别和所述第一部件所在的高铁线路,以进行缺陷预警;针对所述第二部件,确定所述第二部件所属的目标第一部件,输出所述第二部件的缺陷类型、所述第二部件在所述待检测图像中的位置、所述第二部件的类别、所述目标第一部件在所述待检测图像中的位置、所述目标第一部件的类别和所述第二部件所在的高铁线路,以进行缺陷预警。
- 根据权利要求1所述的方法,所述获取高铁接触网的待检测图像,包括:获取成像设备采集的高铁接触网原始图像;对所述高铁接触网原始图像进行过滤,得到所述待检测图像。
- 一种缺陷检测装置,其特征在于,所述装置包括:图像获取模块,用于获取高铁接触网的待检测图像;第一检测模块,用于从所述待检测图像中分割出高铁接触网的第一部件的图像;第二检测模块,用于从所述第一部件的图像中分割出高铁接触网的第二部件的图像;所述第二部件为所述第一部件的子部件;缺陷分类模块,用于基于所述第一部件的图像、所述第二部件的图像对所述第一部件、所述第二部件进行缺陷分类,得到缺陷分类结果。
- 根据权利要求11所述的装置,其特征在于,在从所述待检测图像中分割出高铁接触网的第一部件的图像方面,所述第一检测模块具体用于:对所述待检测图像进行特征提取,得到第一特征图;基于所述第一特征图对所述第一部件进行定位和分类,得到所述第一部件的第一矩形检测框;按照所述第一矩形检测框从所述待检测图像中分割出所述第一部件的图像。
- 根据权利要求12所述的装置,其特征在于,在基于所述第一特征图对所述第一部件进行定位和分类,得到所述第一部件的第一矩形检测框方面,所述第一检测模块具体用于:在所述第一特征图上进行所述第一部件的候选区域坐标预测和前、背景分类,确定出所述第一部件的前景目标;将所述第一部件的前景目标在所述第一特征图中对应的特征进行池化处理,得到第一池化特征;基于所述第一池化特征对所述第一部件进行分类,得到所述第一部件的类别和所述第一矩形检测框。
- 根据权利要求13所述的装置,其特征在于,在从所述第一部件的图像中分割出高 铁接触网的第二部件的图像方面,所述第二检测模块具体用于:对所述第一部件的图像进行伽马校验,得到所述第一部件的待分割图像;从所述第一部件的待分割图像中分割出所述第二部件的图像。
- 根据权利要求13所述的装置,其特征在于,在从所述第一部件的待分割图像中分割出所述第二部件的图像方面,所述第二检测模块具体用于:对所述第一部件的待分割图像进行特征提取,得到第二特征图;基于所述第二特征图对所述第二部件进行定位和分类,得到所述第二部件的第二矩形检测框;按照所述第二矩形检测框从所述第一部件的待分割图像中分割出所述第二部件的图像。
- 根据权利要求15所述的装置,其特征在于,在基于所述第二特征图对所述第二部件进行定位和分类,得到所述第二部件的第二矩形检测框方面,所述第二检测模块具体用于:在所述第二特征图上进行所述第二部件的候选区域坐标预测和前、背景分类,确定出所述第二部件的前景目标;将所述第二部件的前景目标在所述第二特征图中对应的特征进行池化处理,得到第二池化特征;基于所述第二池化特征对所述第二部件进行分类,得到所述第二部件的类别和所述第二矩形检测框。
- 根据权利要求16所述的装置,其特征在于,在基于所述第一部件的图像、所述第二部件的图像对所述第一部件、所述第二部件进行缺陷分类,得到缺陷分类结果方面,所述缺陷分类模块具体用于:分别对所述第一部件的图像、所述第二部件的图像进行特征提取;基于所述第一部件的图像提取出的特征对所述第一部件进行缺陷类型预测,得到所述第一部件的缺陷分类结果;所述第一部件的缺陷分类结果包括所述第一部件的缺陷类型和所述第一部件的类别;以及,基于所述第二部件的图像提取出的特征对所述第二部件进行缺陷类型预测,得到所述第二部件的缺陷分类结果;所述第二部件的缺陷分类结果包括所述第二部件的缺陷类型和所述第二部件的类别。
- 根据权利要求17所述的装置,其特征在于,所述装置还包括缺陷预警模块,所述缺陷预警模块具体用于:根据所述缺陷分类结果进行缺陷预警。
- 根据权利要求18所述的装置,其特征在于,在根据所述缺陷分类结果进行缺陷预警方面,所述缺陷预警模块具体用于:针对所述第一部件,输出所述第一部件的缺陷类型、所述第一部件在所述待检测图像中的位置、所述第一部件的类别和所述第一部件所在的高铁线路,以进行缺陷预警;针对所述第二部件,确定所述第二部件所属的目标第一部件,输出所述第二部件的缺陷类型、所述第二部件在所述待检测图像中的位置、所述第二部件的类别、所述目标第一部件在所述待检测图像中的位置、所述目标第一部件的类别和所述第二部件所在的高铁线路,以进行缺陷预警。
- 根据权利要求11所述的装置,其特征在于,在获取高铁接触网的待检测图像方面,所述图像获取模块具体用于:获取成像设备采集的高铁接触网原始图像;对所述高铁接触网原始图像进行过滤,得到所述待检测图像。
- 一种电子设备,包括输入设备和输出设备,其特征在于,还包括:处理器,适于实现一条或多条指令;以及,计算机存储介质,所述计算机存储介质存储有一条或多条指令,所述一条或多条指令适于由所述处理器加载并执行如权利要求1-10任一项所述的方法。
- 一种计算机存储介质,其特征在于,所述计算机存储介质存储有一条或多条指令,所述一条或多条指令适于由处理器加载并执行如权利要求1-10任一项所述的方法。
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