CN114155375A - Method and device for detecting airport pavement diseases, electronic equipment and storage medium - Google Patents
Method and device for detecting airport pavement diseases, electronic equipment and storage medium Download PDFInfo
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
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Abstract
The invention discloses a method and a device for detecting airport pavement diseases, electronic equipment and a storage medium, wherein the method comprises the following steps: acquiring an airport pavement disease image to be detected; inputting the airport pavement disease image to be detected into a trained disease detection model to obtain extraction results of areas corresponding to different types of diseases in the airport pavement disease image to be detected; the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images. The invention realizes the pixel-level segmentation of various diseases in the airport pavement image through the disease detection model, and improves the segmentation precision.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for detecting airport pavement diseases, electronic equipment and a storage medium.
Background
The safety detection of the airport pavement is the central importance of the civil aviation operation safety, and the current detection of the airport pavement diseases is mainly carried out in a manual mode.
Some of the existing disease detection methods propose a secondary threshold segmentation algorithm based on morphology, so that the segmentation of cracks in an image containing a runway marker is realized, and some of the existing disease detection methods use a seed growth algorithm to detect the cracks on multiple scales, and the segmentation results of the cracks are obtained by matching and fusing crack regions on different scales. Most of the methods depend on parameters of manual design, have poor generalization capability, cannot process complicated and variable airport pavement pictures, and have high detection precision influenced by the environment. With the rise of deep learning, more and more disease detection methods based on deep learning are proposed. For example, the full convolution network is applied to crack segmentation, and the network is trained to segment and predict cracks with different scales by inputting multiple types of crack pictures. However, this method only realizes the disease detection based on the rectangular frame, and cannot realize more refined pixel level segmentation. In addition, the shapes of different types of diseases cannot be reflected, and the position information still has larger deviation.
In summary, a method for detecting an airport pavement disease is needed to solve the above problems in the prior art.
Disclosure of Invention
Because the existing method has the problems, the invention provides a method, a device, electronic equipment and a storage medium for detecting airport pavement diseases.
In a first aspect, the present invention provides a method for detecting an airport pavement disease, comprising:
acquiring an airport pavement disease image to be detected;
inputting the airport pavement disease image to be detected into a trained disease detection model to obtain extraction results of areas corresponding to different types of diseases in the airport pavement disease image to be detected;
the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images.
Further, before inputting the image of the airport pavement disease to be detected into the trained disease detection model and obtaining the extraction result of the region corresponding to the disease in the image of the airport pavement disease to be detected, the method further comprises the following steps:
acquiring a disease training image, a semantic segmentation true value image and a semantic edge true value image;
performing wavelet transformation on the disease training image to obtain a low-frequency characteristic diagram;
extracting image features of the disease training image by adopting a residual error network to obtain a semantic edge feature map;
coding the disease training image according to the low-frequency feature map and the semantic edge feature map based on a U-Net network to obtain a first feature map;
decoding the first feature map according to the low-frequency feature map and the semantic edge feature map to obtain a semantic segmentation prediction map;
determining a first loss function according to the semantic segmentation true value graph and the semantic segmentation prediction graph;
and updating parameters of the disease detection model according to the first loss function to obtain a trained disease detection model.
Further, the obtaining of the semantic segmentation true value map and the semantic edge true value map includes:
extracting the outline of the disease in the disease training image to obtain image labeling information;
determining a semantic segmentation true value graph according to the image annotation information;
and extracting semantic edges of the disease area based on the semantic segmentation true value graph to obtain a semantic edge true value graph.
Further, after the image feature extraction is performed on the disease training image by using the residual error network to obtain a semantic edge feature map, the method further includes:
obtaining a channel domain weight matrix of the semantic edge feature map by adopting an attention mechanism;
obtaining a semantic edge prediction graph according to the semantic edge feature graph and the channel domain weight matrix;
determining a second loss function according to the semantic edge true value graph and the semantic edge prediction graph;
and updating parameters of the disease detection model according to the second loss function.
Further, after the obtaining the first feature map and before the encoding the first feature map according to the low-frequency feature map and the semantic edge feature map, the method further includes:
obtaining a spatial domain weight matrix of the first characteristic diagram by adopting an attention mechanism;
and determining a second feature map according to the first feature map and the spatial domain weight matrix.
Further, the types of the diseases in the image of the airport pavement disease to be detected are cracks, repairs, patches, floor lights and plate seams.
In a second aspect, the present invention provides an airport pavement disease detection device, including:
the acquisition module is used for acquiring an airport pavement disease image to be detected;
the processing module is used for inputting the airport pavement disease image to be detected into a trained disease detection model to obtain extraction results of areas corresponding to different types of diseases in the airport pavement disease image to be detected; the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images.
Further, the processing module is further configured to:
before inputting the image of the airport pavement disease to be detected into a trained disease detection model and obtaining an extraction result of a region corresponding to the disease in the image of the airport pavement disease to be detected, acquiring a disease training image, a semantic segmentation true value image and a semantic edge true value image;
performing wavelet transformation on the disease training image to obtain a low-frequency characteristic diagram;
extracting image features of the disease training image by adopting a residual error network to obtain a semantic edge feature map;
coding the disease training image according to the low-frequency feature map and the semantic edge feature map based on a U-Net network to obtain a first feature map;
decoding the first feature map according to the low-frequency feature map and the semantic edge feature map to obtain a semantic segmentation prediction map;
determining a first loss function according to the semantic segmentation true value graph and the semantic segmentation prediction graph;
and updating parameters of the disease detection model according to the first loss function to obtain a trained disease detection model.
Further, the processing module is specifically configured to:
extracting the outline of the disease in the disease training image to obtain image labeling information;
determining a semantic segmentation true value graph according to the image annotation information;
and extracting semantic edges of the disease area based on the semantic segmentation true value graph to obtain a semantic edge true value graph.
Further, the processing module is further configured to:
obtaining a channel domain weight matrix of the semantic edge feature map by adopting an attention mechanism;
obtaining a semantic edge prediction graph according to the semantic edge feature graph and the channel domain weight matrix;
determining a second loss function according to the semantic edge true value graph and the semantic edge prediction graph;
and updating parameters of the disease detection model according to the second loss function.
Further, the processing module is further configured to:
after the first feature map is obtained and before the first feature map is decoded according to the low-frequency feature map and the semantic edge feature map, obtaining a spatial domain weight matrix of the first feature map by adopting an attention mechanism;
and determining a second feature map according to the first feature map and the spatial domain weight matrix.
Further, the processing module is specifically configured to:
the types of the diseases in the airport pavement disease image to be detected are cracks, repairs, patches, floor lights and plate seams.
In a third aspect, the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the method for detecting an airport pavement disease according to the first aspect is implemented.
In a fourth aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for airport pavement disease detection as described in the first aspect.
According to the technical scheme, the airport pavement disease detection method, the airport pavement disease detection device, the electronic equipment and the storage medium realize pixel-level segmentation of various diseases in the airport pavement image through the disease detection model, and improve the segmentation precision.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a system framework of the method for detecting airport pavement diseases provided by the present invention;
FIG. 2 is a schematic flow chart of a method for detecting airport pavement diseases provided by the present invention;
FIG. 3 is a schematic flow chart of a method for detecting airport pavement diseases provided by the present invention;
FIG. 4 is a schematic diagram of a wavelet transform provided by the present invention;
FIG. 5 is a schematic diagram of a residual error network provided by the present invention;
FIG. 6 is a schematic diagram of a residual error network provided by the present invention;
FIG. 7 is a schematic diagram of a U-Net network provided by the present invention;
FIG. 8 is a schematic diagram of a U-Net network provided by the present invention;
FIG. 9 is a schematic view of an airport pavement disease detection apparatus provided by the present invention;
fig. 10 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The method for detecting the airport pavement diseases provided by the embodiment of the invention can be applied to a system architecture as shown in fig. 1, wherein the system architecture comprises an image sensor 100 and a disease detection model 200.
Specifically, the image sensor 100 is used for acquiring an image of an airport pavement defect to be detected.
The disease detection model 200 is used for obtaining an extraction result of a region corresponding to a disease in an airport pavement disease image to be detected after the airport pavement disease image to be detected is input.
It should be noted that the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value maps and semantic edge true value maps corresponding to the different airport pavement disease images.
It should be noted that fig. 1 is only an example of a system architecture according to the embodiment of the present invention, and the present invention is not limited to this specifically.
Based on the above-mentioned schematic system architecture, fig. 2 is a schematic flow chart corresponding to a method for detecting an airport pavement disease provided by an embodiment of the present invention, as shown in fig. 2, the method includes:
Specifically, an image of an airport pavement defect in a real environment is captured by an image sensor such as a visible light camera.
It should be noted that the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images.
According to the scheme, the pixel-level segmentation of various diseases in the airport pavement image is realized through the disease detection model, and the segmentation precision is improved.
Before step 202, the embodiment of the present invention has a step flow as shown in fig. 3, which is specifically as follows:
Specifically, extracting an outline of a disease in a disease training image to obtain image labeling information;
determining a semantic segmentation true value graph according to the image annotation information;
and extracting semantic edges of the disease area based on the semantic segmentation true value graph to obtain a semantic edge true value graph.
In a possible implementation mode, LabelMe is adopted to carry out manual labeling on the disease training image to obtain a semantic segmentation true value image of the airport pavement disease image, and then Matlab is adopted to extract a corresponding semantic edge true value image from the semantic segmentation true value image.
In the embodiment of the invention, the types of the diseases in the airport pavement disease image to be detected are cracks, repairs, patches, floor lights and plate seams.
According to the scheme, besides three diseases of cracks, repair and patches, the common typical categories of the ground light and the slab joint in the airport pavement are considered to better detect the airport pavement, and more detailed information is provided.
And step 302, performing wavelet transformation on the disease training image to obtain a low-frequency characteristic diagram.
In one possible implementation, a haar wavelet transform is performed on the lesion training image.
Specifically, as shown in fig. 4, four-level haar wavelet transform is performed on the input lesion training image, and the low-frequency part generated by each level of wavelet transform is used as effective frequency domain information and is used as the input of the next level of wavelet transform, thereby obtaining LL1, LL2, LL3, and LL 4.
According to the scheme, the noise of the disease training image under the complex background is reduced by extracting the multi-scale low-frequency information.
And 303, extracting image features of the disease training image by using a residual error network to obtain a semantic edge feature map.
In one possible implementation, the residual error network ResNet101 is used for image feature extraction of the disease training image.
Further, obtaining a channel domain weight matrix of the semantic edge feature map by adopting an attention mechanism;
obtaining a semantic edge prediction graph according to the semantic edge feature graph and the channel domain weight matrix;
determining a second loss function according to the semantic edge true value graph and the semantic edge prediction graph;
and updating parameters of the disease detection model according to the second loss function.
As shown in fig. 5, the outputs of the five parts of the residual network are feature maps c1, c2, c3, c4 and c5 of different scales respectively.
Specifically, the output result of the first branch is a convolution operation performed on c 5. And the second branch deconvolves the four characteristic graphs of c1, c2, c3 and c5 into the same size, and splices the characteristic graphs together, and finally outputs a rough semantic edge prediction result through convolution operation.
In one possible implementation, the semantic edge prediction graph is compared with the semantic edge true value graph to calculate a weighted cross entropy loss and perform back propagation.
In the embodiment of the present invention, a specific calculation formula of the second loss function is as follows:
wherein M represents the number of categories; w is acA weight representing the category c; y isicIs a sign function; n is the number of samples; if the real category of the sample i is equal to c, 1 is taken, otherwise 0 is taken; p is a radical oficRepresenting the predicted probability that the observed sample i belongs to class c.
The scheme can accelerate the convergence of the network, and the influence of noise contained in c1, c2, c3 and c4 on the corresponding predicted branch of c5 is reduced.
By the scheme, the problems that semantic edges are fine and small in number, and the occupation ratio of the foreground and the background in the data set is extremely unbalanced are solved.
Further, as shown in fig. 6, since the acquired image disease edge has a small proportion and generally only occupies one pixel, in order to improve the prediction effect of the semantic edge, the embodiment of the present invention adds a channel domain attention mechanism after the c5 prediction branch, that is, a channel domain weight matrix of the feature map c5 is obtained through the global pooling layer and the full connection layer, and the output of c5 multiplied by the weight matrix is convolved to obtain the semantic edge segmentation result.
According to the scheme, the bottom layer characteristics of the residual error network are fused based on the residual error network, so that the semantic segmentation result is less influenced by the noise of the bottom layer characteristics. The addition of the attention mechanism enables the network to better capture the fine edge features, thereby obtaining more accurate prediction results.
And 304, coding the disease training image according to the low-frequency feature map and the semantic edge feature map based on the U-Net network to obtain a first feature map.
As shown in fig. 7, the encoding portion is composed of four convolution blocks, in the diagram, c1, c2, c3 and c5 are semantic edge feature maps, and LL1, LL2, LL3 and LL4 are low-frequency feature maps.
It should be noted that each convolution operation halves the feature map side length.
For example, the input picture size is 512 × 512, and the feature size after four convolution operations is 32 × 32.
And 305, decoding the first feature map according to the low-frequency feature map and the semantic edge feature map to obtain a semantic segmentation prediction map.
Further, the decoding portion includes four deconvolution layers, each deconvolution operation doubles the feature edge length, such as an input feature size of 64 × 64, and a feature size after four deconvolution operations of 512 × 512.
Based on the method, the disease training image passes through a U-Net network to obtain a pixel-level semantic segmentation prediction image with the same size.
In one possible implementation, a weighted cross-entropy penalty is calculated.
And 307, updating parameters of the disease detection model according to the first loss function to obtain the trained disease detection model.
According to the scheme, the wavelet transformation is adopted to extract the low-frequency information of the image, and the low-frequency information is fused through the U-Net network, so that the noise elimination is facilitated, and the semantic segmentation precision is improved. The multi-scale frequency domain information features, the semantic edge features and the image encoder extraction features are fused based on the U-Net network, high-precision pixel-level segmentation of various diseases in the airport pavement image is achieved, and the precision of semantic segmentation results and the generalization performance of models are improved.
Further, before step 305, the embodiment of the present invention obtains a spatial domain weight matrix of the first feature map by using an attention mechanism;
and determining a second characteristic diagram according to the first characteristic diagram and the spatial domain weight matrix.
Specifically, as shown in fig. 8, a spatial domain attention mechanism is immediately followed by the encoding portion, a spatial domain weight matrix of the first feature map is obtained by convolution operation, and the weighted feature map, that is, the second feature map, is output after the first feature map is multiplied by the weight matrix.
According to the scheme, an attention mechanism is added between the encoder and the decoder to perform weighting processing on the fusion features, and the precision of the semantic segmentation result is improved.
Based on the same inventive concept, fig. 9 exemplarily shows an airport pavement disease detection apparatus provided by an embodiment of the present invention, which can be a flow of an airport pavement disease detection method.
The apparatus, comprising:
an obtaining module 901, configured to obtain an image of a road surface defect of an airport to be detected;
the processing module 902 is configured to input the image of the airport pavement disease to be detected into a trained disease detection model, so as to obtain extraction results of areas corresponding to different types of diseases in the image of the airport pavement disease to be detected; the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images.
Further, the processing module 902 is further configured to:
before inputting the image of the airport pavement disease to be detected into a trained disease detection model and obtaining an extraction result of a region corresponding to the disease in the image of the airport pavement disease to be detected, acquiring a disease training image, a semantic segmentation true value image and a semantic edge true value image;
performing wavelet transformation on the disease training image to obtain a low-frequency characteristic diagram;
extracting image features of the disease training image by adopting a residual error network to obtain a semantic edge feature map;
coding the disease training image according to the low-frequency feature map and the semantic edge feature map based on a U-Net network to obtain a first feature map;
decoding the first feature map according to the low-frequency feature map and the semantic edge feature map to obtain a semantic segmentation prediction map;
determining a first loss function according to the semantic segmentation true value graph and the semantic segmentation prediction graph;
and updating parameters of the disease detection model according to the first loss function to obtain a trained disease detection model.
Further, the processing module 902 is specifically configured to:
extracting the outline of the disease in the disease training image to obtain image labeling information;
determining a semantic segmentation true value graph according to the image annotation information;
and extracting semantic edges of the disease area based on the semantic segmentation true value graph to obtain a semantic edge true value graph.
Further, the processing module 902 is further configured to:
after the residual error network is adopted to extract the image characteristics of the disease training image to obtain a semantic edge characteristic diagram, an attention mechanism is adopted to obtain a channel domain weight matrix of the semantic edge characteristic diagram;
obtaining a semantic edge prediction graph according to the semantic edge feature graph and the channel domain weight matrix;
determining a second loss function according to the semantic edge true value graph and the semantic edge prediction graph;
and updating parameters of the disease detection model according to the second loss function.
Further, the processing module 902 is further configured to:
after the first feature map is obtained and before the first feature map is decoded according to the low-frequency feature map and the semantic edge feature map, obtaining a spatial domain weight matrix of the first feature map by adopting an attention mechanism;
and determining a second feature map according to the first feature map and the spatial domain weight matrix.
Further, the processing module 902 is specifically configured to:
the types of the diseases in the airport pavement disease image to be detected are cracks, repairs, patches, floor lights and plate seams.
Based on the same inventive concept, another embodiment of the present invention provides an electronic device, which specifically includes the following components, with reference to fig. 10: a processor 1001, a memory 1002, a communication interface 1003, and a communication bus 1004;
the processor 1001, the memory 1002 and the communication interface 1003 complete mutual communication through the communication bus 1004; the communication interface 1003 is used for realizing information transmission among the devices;
the processor 1001 is configured to call a computer program in the memory 1002, and when the processor executes the computer program, the processor implements all the steps of the above method for detecting an airport pavement disease, for example, when the processor executes the computer program, the processor implements the following steps: acquiring an airport pavement disease image to be detected; inputting the airport pavement disease image to be detected into a trained disease detection model to obtain an extraction result of a region corresponding to the disease in the airport pavement disease image to be detected; the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images.
Based on the same inventive concept, a further embodiment of the present invention provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor implements all the steps of the above-mentioned method for detecting airport pavement diseases, for example, the processor implements the following steps when executing the computer program: acquiring an airport pavement disease image to be detected; inputting the airport pavement disease image to be detected into a trained disease detection model to obtain an extraction result of a region corresponding to the disease in the airport pavement disease image to be detected; the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images.
In addition, the logic instructions in the memory may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, an apparatus for detecting airport pavement diseases, or a network device) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and 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 modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on such understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes instructions for enabling a computer device (which may be a personal computer, an airport pavement disease detection apparatus, or a network device, etc.) to execute the airport pavement disease detection method described in each embodiment or some portions of the embodiments.
In addition, in the present invention, terms such as "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Moreover, in the present invention, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Furthermore, in the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will 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 of the embodiments of the present invention.
Claims (10)
1. A method for detecting airport pavement diseases is characterized by comprising the following steps:
acquiring an airport pavement disease image to be detected;
inputting the airport pavement disease image to be detected into a trained disease detection model to obtain extraction results of areas corresponding to different types of diseases in the airport pavement disease image to be detected;
the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images.
2. The method for detecting the airport pavement diseases according to claim 1, wherein before inputting the image of the airport pavement diseases to be detected into a trained disease detection model and obtaining an extraction result of a region corresponding to the diseases in the image of the airport pavement diseases to be detected, the method further comprises:
acquiring a disease training image, a semantic segmentation true value image and a semantic edge true value image;
performing wavelet transformation on the disease training image to obtain a low-frequency characteristic diagram;
extracting image features of the disease training image by adopting a residual error network to obtain a semantic edge feature map;
coding the disease training image according to the low-frequency feature map and the semantic edge feature map based on a U-Net network to obtain a first feature map;
decoding the first feature map according to the low-frequency feature map and the semantic edge feature map to obtain a semantic segmentation prediction map;
determining a first loss function according to the semantic segmentation true value graph and the semantic segmentation prediction graph;
and updating parameters of the disease detection model according to the first loss function to obtain a trained disease detection model.
3. The method of airport pavement disease detection as claimed in claim 2, wherein said obtaining semantic segmentation true value map and semantic edge true value map comprises:
extracting the outline of the disease in the disease training image to obtain image labeling information;
determining a semantic segmentation true value graph according to the image annotation information;
and extracting semantic edges of the disease area based on the semantic segmentation true value graph to obtain a semantic edge true value graph.
4. The method for detecting airport pavement diseases according to claim 2, wherein after the image feature extraction is performed on the disease training image by using a residual error network to obtain a semantic edge feature map, the method further comprises:
obtaining a channel domain weight matrix of the semantic edge feature map by adopting an attention mechanism;
obtaining a semantic edge prediction graph according to the semantic edge feature graph and the channel domain weight matrix;
determining a second loss function according to the semantic edge true value graph and the semantic edge prediction graph;
and updating parameters of the disease detection model according to the second loss function.
5. The method for detecting airport pavement diseases according to claim 2, wherein after the obtaining the first feature map and before the encoding the first feature map according to the low-frequency feature map and the semantic edge feature map, further comprising:
obtaining a spatial domain weight matrix of the first characteristic diagram by adopting an attention mechanism;
and determining a second feature map according to the first feature map and the spatial domain weight matrix.
6. The method for detecting the airport pavement diseases according to claim 1, wherein the types of the diseases in the image of the airport pavement diseases to be detected are cracks, repairs, patches, floor lights and plate seams.
7. The utility model provides a device that airport pavement disease detected which characterized in that includes:
the acquisition module is used for acquiring an airport pavement disease image to be detected;
the processing module is used for inputting the airport pavement disease image to be detected into a trained disease detection model to obtain extraction results of areas corresponding to different types of diseases in the airport pavement disease image to be detected; the trained disease detection model is obtained by training different airport pavement disease images and semantic segmentation true value images and semantic edge true value images corresponding to the different airport pavement disease images.
8. A computer program product comprising a computer program, characterized in that the computer program realizes the steps of the method according to any one of claims 1 to 6 when executed by a processor.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1 to 6 are implemented when the processor executes the program.
10. A non-transitory computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114463187A (en) * | 2022-04-14 | 2022-05-10 | 合肥高维数据技术有限公司 | Image semantic segmentation method and system based on aggregation edge features |
| CN114594103A (en) * | 2022-04-12 | 2022-06-07 | 四川大学 | Method and system for automatically detecting surface defects of nuclear industrial equipment and automatically generating reports |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114594103A (en) * | 2022-04-12 | 2022-06-07 | 四川大学 | Method and system for automatically detecting surface defects of nuclear industrial equipment and automatically generating reports |
| CN114594103B (en) * | 2022-04-12 | 2023-05-16 | 四川大学 | Automatic detection and report generation method and system for surface defects of nuclear industrial equipment |
| CN114463187A (en) * | 2022-04-14 | 2022-05-10 | 合肥高维数据技术有限公司 | Image semantic segmentation method and system based on aggregation edge features |
| CN114463187B (en) * | 2022-04-14 | 2022-06-17 | 合肥高维数据技术有限公司 | Image semantic segmentation method and system based on aggregation edge features |
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