CN114418950A - Road disease detection method, device, equipment and storage medium - Google Patents
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Abstract
The invention belongs to the technical field of artificial intelligence and discloses a road disease detection method, a device, equipment and a storage medium. The method comprises the following steps: acquiring an image to be detected; segmenting the image to be detected to obtain a road image; inputting the road image into a target detection model to obtain a detection result; and judging whether the road corresponding to the image to be detected has diseases or not according to the detection result. By the mode, the detection result of the road is obtained by training the image input value to be detected in the target detection model, the detection result contains the diseases existing in the road, the detection efficiency of the road diseases is improved, and road managers can repair the road diseases according to the detection result, so that the safety of the road is improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a road disease detection method, a road disease detection device, road disease detection equipment and a storage medium.
Background
While the newly-built expressway is rapidly increased, many of the original expressways are damaged and damaged to different degrees. And the diseases and damages can not be timely and reasonably maintained on the road surface, so that the road condition of part of the expressway is sharply reduced, and the comfort, the economy and the safety of vehicle driving are not influenced a little. As a result, highway pavement maintenance management and its associated problems have received increasing attention in recent years.
Meanwhile, accidents such as loss of inspection well covers and serious road diseases caused by overloading of heavy vehicles frequently occur in various places due to untimely road maintenance.
Disclosure of Invention
The invention mainly aims to provide a road disease detection method, a road disease detection device, road disease detection equipment and a storage medium, and aims to solve the technical problem of improving the road disease detection efficiency in the prior art.
In order to achieve the aim, the invention provides a road disease detection method, which comprises the following steps:
acquiring an image to be detected;
segmenting the image to be detected to obtain a road image;
inputting the road image into a target detection model to obtain a detection result;
and judging whether the road corresponding to the image to be detected has diseases or not according to the detection result.
Optionally, before the step of inputting the road image into the target detection model and obtaining the detection result, the method further includes:
acquiring an initial training data set;
obtaining a target training data set according to the initial training data set;
and training an initial detection model according to the target training data set to obtain a target detection model.
Optionally, the step of obtaining a target training data set according to the initial training data set includes:
determining an initial training image according to the initial training data set;
obtaining a training image pair according to the initial training image;
and obtaining a target training data set according to the training image pair.
Optionally, the step of obtaining a target training data set according to the training image pair includes:
inputting the training image pair to a preset training model to obtain a pseudo label image pair;
determining a confidence level of the pseudo-tag image pair;
screening the pseudo label image pair according to the confidence coefficient to obtain a target training image;
and obtaining a target training data set according to the target training image.
Optionally, the step of acquiring an image to be detected includes:
acquiring a video to be detected;
obtaining a frame image set according to the video to be detected;
determining adjacent frame images according to the frame image set;
and fusing the adjacent frame images into an image to be detected.
Optionally, the step of segmenting the image to be detected to obtain a road image includes:
converting the image to be detected into a gray image;
carrying out noise reduction processing on the gray level image to obtain a noise reduction image;
determining the gradient value of each pixel point in the noise-reduced image, and obtaining a road edge image according to the gradient value;
and determining a road image according to the road edge image.
Optionally, after the step of determining whether a road corresponding to the image to be detected has a disease according to the detection result, the method further includes:
when the road corresponding to the image to be detected has a disease, acquiring position information and a disease type corresponding to the road;
generating a road repairing scheme according to the disease type;
and generating a road disease report according to the position information, the disease type and the road repairing scheme.
In addition, in order to achieve the above object, the present invention also provides a road damage detecting device, including:
the acquisition module is used for acquiring an image to be detected;
the segmentation module is used for segmenting and segmenting the image to be detected to obtain a road image;
the detection module is used for inputting the road image into a target detection model to obtain a detection result;
and the judging module is used for judging whether the road corresponding to the image to be detected has diseases or not according to the detection result.
Further, in order to achieve the above object, the present invention also provides a road disease detection apparatus including: a memory, a processor and a road damage detection program stored on the memory and operable on the processor, the road damage detection program being configured to implement the steps of the road damage detection method as described above.
Furthermore, to achieve the above object, the present invention also proposes a storage medium having stored thereon a road damage detection program which, when executed by a processor, implements the steps of the road damage detection method as described above.
The method comprises the steps of obtaining an image to be detected; segmenting the image to be detected to obtain a road image; inputting the road image into a target detection model to obtain a detection result; and judging whether the road corresponding to the image to be detected has diseases or not according to the detection result. By the method, in the target detection model which is trained by the image input value to be detected, the target detection model analyzes the image to be detected through calculation, so that the detection result of the road is obtained, the detection result contains diseases existing in the road, and road management personnel can repair the road diseases according to the detection result, so that the safety of the road is improved.
Drawings
FIG. 1 is a schematic structural diagram of a road damage detection device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a road disease detection method according to a first embodiment of the present invention;
FIG. 3 is a schematic flow chart of a road disease detection method according to a second embodiment of the present invention;
fig. 4 is a block diagram showing a configuration of a road damage detecting apparatus according to a first embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a road damage detection device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the road damage detecting apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of a road hazard detection apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a road damage detection program.
In the road damage detection apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the road damage detection apparatus of the present invention may be provided in the road damage detection apparatus which calls the road damage detection program stored in the memory 1005 through the processor 1001 and executes the road damage detection method provided by the embodiment of the present invention.
An embodiment of the present invention provides a road disease detection method, and referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the road disease detection method of the present invention.
In this embodiment, the road disease detection method includes the following steps:
step S10: and acquiring an image to be detected.
It should be noted that the execution subject of the embodiment is a terminal device, for example, a device such as a computer that can perform data processing and run an application program.
In specific implementation, the image to be detected comprises an image which needs to be subjected to road disease detection. Treat that the image of examining is gathered by the camera that sets up on mobile device, and mobile device can be vehicle or unmanned aerial vehicle.
Further, in order to improve the efficiency of the terminal device in detection, step S10 includes: acquiring a video to be detected; obtaining a frame image set according to the video to be detected; determining adjacent frame images according to the frame image set; and fusing the adjacent frame images into an image to be detected.
It can be understood that when the camera captures the road image, the video is usually shot on the road according to the driving direction on the road, but the video is composed of multiple frames of images, so that the multiple frames of continuous images contain the same road image, and the detection required for each frame leads to resource waste.
In the specific implementation, the video to be detected is decoded and framed, and is decomposed into a plurality of continuous frames of images, i.e. a frame image set, and then frame images including the same road image are fused, generally, adjacent frame images include more repeated road images, for example, each adjacent 10 frames are a group of frame images to be fused, and then 10 frame images are fused, so as to obtain an image to be detected.
It should be noted that the essence of the fusion of multiple images is to stitch multiple images into one image, when stitching the images, the adjacent frame images are preprocessed first, and after the acquired images are corrected by the geometric deformation correction method, images of the same scenery in the image overlapping area have the same shape and the same spatial relative position. When the camera is used for collecting road images, the images can be shot perpendicular to the ground and keep the same height, so that the visual angles of the roads in the images are consistent, but the visual angles of the camera can be slightly changed due to the fact that the ground is hollow or a deceleration strip exists, and the images need to be corrected.
After image correction, taking the intermediate image of the adjacent frame image group as a reference image, and searching the feature points of the rest frame images, which are the same as the reference image, wherein the feature points comprise: closed areas, contour and edge areas, corners, lines, etc., are usually matched on roads with the road course as a feature point. And after the matched characteristic points are determined, splicing the plurality of frame images into one image to be detected according to the characteristic points.
Step S20: and segmenting the image to be detected to obtain a road image.
It should be noted that, in order to completely shoot the road, the image acquired by the camera usually includes some images outside the road, and in order to avoid the influence of the images outside the road on the detection result, the images outside the road need to be segmented, and only the images including the road, that is, the road images, are left.
The embodiment realizes the segmentation of the road image by detecting the edge of the road, firstly, the image is transformed into a frequency domain from a space domain through Fourier transform, and a high-frequency part is determined according to the frequency domain, the high-frequency part generally corresponds to the edge part in the image, the lane line can be used as the road edge for detection on the urban asphalt road, and the color difference between the cement ground and the surrounding environment is generally larger, so that the good segmentation effect can be achieved.
Further, step S20 further includes: converting the image to be detected into a gray image; carrying out noise reduction processing on the gray level image to obtain a noise reduction image; determining the gradient value of each pixel point in the noise-reduced image, and obtaining a road edge image according to the gradient value; and determining a road image according to the road edge image.
Specifically, the image to be detected can be grayed and converted into a gray image, then the gray image is denoised, the gray image can be denoised in a wavelet denoising mode, the gradient value of each pixel point in the denoised gray image is calculated after denoising is finished, the gradient value with the change larger than a preset threshold value is used as a road edge, so that a road edge image is obtained, and the image to be detected is segmented according to an edge line in the road edge image to obtain the road image.
Step S30: and inputting the road image into a target detection model to obtain a detection result.
It should be noted that the target detection model is a trained model, the target detection model may be YOLOv5, and YOLOv5 is smaller than YOLOv4, has a faster operation speed, and can process the road image more efficiently.
In specific implementation, after the road image is input into the target detection model, a detection result can be obtained, wherein the detection result comprises whether a road has a disease or not, and if the road has the disease, the type of the disease is marked.
Step S40: and judging whether the road corresponding to the image to be detected has diseases or not according to the detection result.
It is understood that the types of the diseases include transverse cracks, longitudinal cracks, depressions and the like, and when the above-mentioned diseases are detected on the road, the disease portions are marked and the disease types are displayed. And the road manager can determine the road disease information according to the detection result.
Further, after step S40, the method further includes: when the road corresponding to the image to be detected has a disease, acquiring position information and a disease type corresponding to the road; generating a road repairing scheme according to the disease type; and generating a road disease report according to the position information, the disease type and the road repairing scheme.
It should be noted that, when a road is collected, the geographical position of the road is collected at the same time, so as to form a road-position mapping relationship, and when a road is detected to have a disease, the corresponding position information can be searched according to the mapping relationship, so that road managers can repair the road conveniently. The road information includes a location area where the disease exists.
Similarly, after the disease type of the road is detected, a corresponding road repairing scheme is generated according to the disease type, for example: when the disease type is the cracks of the turtles, the repairing scheme is that sandy soil deposited in the cracks is removed on a road section with the crack diseases, an area to be repaired is separated by using an adhesive tape, aggregate and composite modified emulsion are uniformly filled in the repairing area, and the repairing can be completed after two hours.
In the specific implementation, a road disease report is generated according to the position information, the disease type and the road repairing scheme, the road disease report is sent to a road manager, and the road disease report is uploaded to a cloud for backup.
The embodiment comprises the steps of obtaining an image to be detected; segmenting the image to be detected to obtain a road image; inputting the road image into a target detection model to obtain a detection result; and judging whether the road corresponding to the image to be detected has diseases or not according to the detection result. By the method, in the target detection model which is trained by the image input value to be detected, the target detection model analyzes the image to be detected through calculation, so that the detection result of the road is obtained, the detection result contains diseases existing in the road, and road management personnel can repair the road diseases according to the detection result, so that the safety of the road is improved.
Referring to fig. 3, fig. 3 is a schematic flow chart of a road disease detection method according to a second embodiment of the present invention.
Based on the first embodiment, before the step S30, the method for detecting a road fault in this embodiment further includes:
step S21: an initial training data set is obtained.
It should be noted that the initial training data set includes images of various types of roads, such as urban streets, country lanes, and expressways, under different weather conditions. Since roads in different countries may differ, when detecting road diseases in different regions, an initial training data set of local roads is used.
Step S22: and obtaining a target training data set according to the initial training data set.
Further, in order to make the model training based on richer semantic information for training, step S22 includes: determining an initial training image according to the initial training data set; obtaining a training image pair according to the initial training image; and obtaining a target training data set according to the training image pair.
In a specific implementation, an initial training image is obtained in an initial training data set, the initial training image is a road image in the initial training data set, a road portion in the image is segmented based on the road segmentation method, and then the segmented image is subjected to image enhancement, for example, the segmented image is subjected to mirroring and turning, so that a training image pair is formed, and the training image pair includes the segmented initial training image and a corresponding image-enhanced image.
Further, the step of obtaining a target training data set according to the training image pair includes: inputting the training image pair to a preset training model to obtain a pseudo label image pair; determining a confidence level of the pseudo-tag image pair; screening the pseudo label image pair according to the confidence coefficient to obtain a target training image; and obtaining a target training data set according to the target training image.
It should be noted that the preset training model is used to screen out images with a large uncertainty in the training images, so as to prevent resource waste caused by training the initial detection model.
In this embodiment, the value range of the pseudo label is set to [0, 1], and the calculation process of the training model on the training image pair is preset as follows:
wherein, giFor training the pseudo-label value, p, of an image in a pairiIn order to be able to act as a probability for the training image,
、
is a false tag threshold value, wherein
。
When p isiIs greater than or equal to
When g isiIs 1 when p isiIs less than or equal to
When g isiIs 0.
Wherein, the loss function of the preset training model is as follows:
wherein s is the number of pseudo tags,
to train the pseudo-label for either image of the pair,
the original output probability of the training model is preset.
It will be appreciated that a pseudo label value for a training image other than 1 or 0 needs to be determined by a loss function. And determining confidence according to the pseudo tag value:
where β is the confidence and α is a preset value, typically 0.5.
It is understood that the training images with the confidence degrees larger than the confidence degree threshold value are used as target training images, so that the training images capable of being used as training models are screened out.
Further, this embodiment has still gathered a batch of road data that accords with local actual conditions, and the pertinence is stronger and has made the marking software to mark the training image through the marking software, the road defect kind of mark has: transverse cracks, longitudinal cracks, crazing and potholes. A total of more than six thousand multi-label valid data sets are labeled. And obtaining a target training data set after the standard is completed.
Step S23: and training an initial detection model according to the target training data set to obtain a target detection model.
In specific implementation, after the initial detection model is trained through the target training data set, a target detection model for detecting the road diseases can be obtained.
The embodiment obtains an initial training data set; obtaining a target training data set according to the initial training data set; and training an initial detection model according to the target training data set to obtain a target detection model. By the method, model training is based on richer semantic information, so that robustness is better, and the accuracy of target detection is improved.
Furthermore, an embodiment of the present invention further provides a storage medium, where a road damage detection program is stored, and the road damage detection program, when executed by a processor, implements the steps of the road damage detection method as described above.
Since the storage medium adopts all technical solutions of all the embodiments, at least all the beneficial effects brought by the technical solutions of the embodiments are achieved, and no further description is given here.
Referring to fig. 4, fig. 4 is a block diagram of a road damage detection device according to a first embodiment of the present invention.
As shown in fig. 4, a road damage detection device provided in an embodiment of the present invention includes:
the acquisition module 10 is used for acquiring an image to be detected.
And the segmentation module 20 is used for segmenting and segmenting the image to be detected to obtain a road image.
And the detection module 30 is configured to input the road image into a target detection model to obtain a detection result.
And the judging module 40 is used for judging whether the road corresponding to the image to be detected has a disease or not according to the detection result.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
The embodiment is realized by acquiring an image to be detected; segmenting the image to be detected to obtain a road image; inputting the road image into a target detection model to obtain a detection result; and judging whether the road corresponding to the image to be detected has diseases or not according to the detection result. By the method, in the target detection model which is trained by the image input value to be detected, the target detection model analyzes the image to be detected through calculation, so that the detection result of the road is obtained, the detection result contains diseases existing in the road, and road management personnel can repair the road diseases according to the detection result, so that the safety of the road is improved.
In an embodiment, the detecting module 30 is further configured to obtain an initial training data set;
obtaining a target training data set according to the initial training data set;
and training an initial detection model according to the target training data set to obtain a target detection model.
In an embodiment, the detecting module 30 is further configured to determine an initial training image according to the initial training data set;
obtaining a training image pair according to the initial training image;
and obtaining a target training data set according to the training image pair.
In an embodiment, the detecting module 30 is further configured to input the training image pair to a training model to obtain a pseudo label image pair;
determining a confidence level of the pseudo-tag image pair;
screening the pseudo label image pair according to the confidence coefficient to obtain a target training image;
and obtaining a target training data set according to the target training image.
In an embodiment, the obtaining module 10 is further configured to obtain a video to be detected;
obtaining a frame image set according to the video to be detected;
determining adjacent frame images according to the frame image set;
and fusing the adjacent frame images into an image to be detected.
In an embodiment, the segmentation module 20 is further configured to convert the image to be detected into a grayscale image;
carrying out noise reduction processing on the gray level image to obtain a noise reduction image;
determining the gradient value of each pixel point in the noise-reduced image, and obtaining a road edge image according to the gradient value;
and determining a road image according to the road edge image.
In an embodiment, the determining module 40 is further configured to, when a road corresponding to the image to be detected has a disease, obtain position information and a disease type corresponding to the road;
generating a road repairing scheme according to the disease type;
and generating a road disease report according to the position information, the disease type and the road repairing scheme.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment may refer to the road disease detection method provided in any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system 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 system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A road disease detection method is characterized by comprising the following steps:
acquiring an image to be detected;
segmenting the image to be detected to obtain a road image;
inputting the road image into a target detection model to obtain a detection result;
and judging whether the road corresponding to the image to be detected has diseases or not according to the detection result.
2. The method of claim 1, wherein the step of inputting the road image into an object detection model to obtain a detection result further comprises:
acquiring an initial training data set;
obtaining a target training data set according to the initial training data set;
and training an initial detection model according to the target training data set to obtain a target detection model.
3. The method of claim 2, wherein the step of deriving a target training data set from the initial training data set comprises:
determining an initial training image according to the initial training data set;
obtaining a training image pair according to the initial training image;
and obtaining a target training data set according to the training image pair.
4. The method of claim 3, wherein the step of deriving a target training data set from the training image pair comprises:
inputting the training image pair to a preset training model to obtain a pseudo label image pair;
determining a confidence level of the pseudo-tag image pair;
screening the pseudo label image pair according to the confidence coefficient to obtain a target training image;
and obtaining a target training data set according to the target training image.
5. The method of claim 1, wherein the step of acquiring an image to be detected comprises:
acquiring a video to be detected;
obtaining a frame image set according to the video to be detected;
determining adjacent frame images according to the frame image set;
and fusing the adjacent frame images into an image to be detected.
6. The method of claim 1, wherein the step of segmenting the image to be detected to obtain a road image comprises:
converting the image to be detected into a gray image;
carrying out noise reduction processing on the gray level image to obtain a noise reduction image;
determining the gradient value of each pixel point in the noise-reduced image, and obtaining a road edge image according to the gradient value;
and determining a road image according to the road edge image.
7. The method according to any one of claims 1 to 6, wherein after the step of determining whether the road corresponding to the image to be detected has a disease according to the detection result, the method further comprises:
when the road corresponding to the image to be detected has a disease, acquiring position information and a disease type corresponding to the road;
generating a road repairing scheme according to the disease type;
and generating a road disease report according to the position information, the disease type and the road repairing scheme.
8. A road disease detection device, characterized in that, road disease detection device includes:
the acquisition module is used for acquiring an image to be detected;
the segmentation module is used for segmenting and segmenting the image to be detected to obtain a road image;
the detection module is used for inputting the road image into a target detection model to obtain a detection result;
and the judging module is used for judging whether the road corresponding to the image to be detected has diseases or not according to the detection result.
9. A road disease detection apparatus, characterized in that the apparatus comprises: a memory, a processor and a road damage detection program stored on the memory and operable on the processor, the road damage detection program being configured to implement the road damage detection method according to any one of claims 1 to 7.
10. A storage medium having stored thereon a road damage detection program which, when executed by a processor, implements the road damage detection method according to any one of claims 1 to 7.
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| CN115082802A (en) * | 2022-08-18 | 2022-09-20 | 深圳市城市交通规划设计研究中心股份有限公司 | Road disease identification method, device, equipment and readable storage medium |
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