CN110910354A - Road detection vehicle and road detection method and device - Google Patents
Road detection vehicle and road detection method and device Download PDFInfo
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- CN110910354A CN110910354A CN201911079608.7A CN201911079608A CN110910354A CN 110910354 A CN110910354 A CN 110910354A CN 201911079608 A CN201911079608 A CN 201911079608A CN 110910354 A CN110910354 A CN 110910354A
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Abstract
The invention discloses a road detection vehicle, a road detection method and a road detection device, wherein the method comprises the following steps: acquiring a road image; recognizing and extracting a disease target of the road image by adopting a deep learning model; and classifying the extracted disease targets and carrying out parameter statistics. According to the road detection method provided by the embodiment of the invention, the automatic and intelligent analysis of the road video and image data collected by the high-speed running road surface disease detection vehicle is realized by performing multi-lane parallel detection, automatically identifying, classifying and recording the damage information of the road surface.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a road detection vehicle, a road detection method and a road detection device.
Background
The road maintenance management system starts in the sixties and seventies of the 20 th century, and due to the limitation of the technological level, the data acquisition technology is lagged behind, and the data precision is difficult to guarantee. Therefore, intensive research on road inspection technologies has been carried out in various countries, and great progress has been made.
In the related art, general road maintenance detection still mainly takes a manual field investigation method with low efficiency and poor safety, and some road maintenance detection basically enters the actual application stage of automatic road disease detection.
However, the problems of high detection cost, long detection period and time-consuming data processing commonly exist in road maintenance detection in the related art, and the requirement of daily maintenance routing inspection of roads cannot be met.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the first objective of the present invention is to provide a road detection method, which can perform multi-lane parallel detection, automatically identify, classify and record the damage information of the road surface, so as to realize automatic and intelligent analysis of the road surface video and image data collected by the high-speed driving road surface disease detection vehicle.
A second object of the present invention is to provide a road detection device.
The third purpose of the invention is to provide a road detection vehicle.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a road detection method, including: acquiring a road image; recognizing and extracting a disease target of the road image by adopting a deep learning model; and classifying the extracted disease targets and carrying out parameter statistics.
In addition, the road detection method according to the above embodiment of the present invention may further have the following additional technical features:
according to one embodiment of the invention, the deep learning model is a multi-layer convolutional neural network model.
According to one embodiment of the invention, the acquiring the road image comprises: and acquiring road images of a plurality of lanes continuously acquired by the area-array camera.
According to one embodiment of the invention, the area-array camera is arranged obliquely to the road.
According to an embodiment of the present invention, the acquiring the road image further includes: and carrying out image stabilization processing on the road images of the plurality of lanes.
According to an embodiment of the present invention, the acquiring the road image further includes: acquiring the speed of a road detection vehicle corresponding to the road images of the plurality of lanes; smoothly extracting the road image after image stabilization processing according to the speed of the road detection vehicle; and carrying out image splicing and fusion on the smoothly extracted road image.
According to an embodiment of the present invention, the acquiring the road image further includes: and carrying out image visual transformation on the fused road image to obtain a road image under the aerial view angle.
According to an embodiment of the present invention, the acquiring the road image further includes: and preprocessing the road image under the bird's-eye view angle, wherein the preprocessing comprises gray scale correction and/or image enhancement.
According to the road detection method provided by the embodiment of the invention, the road image can be obtained, the deep learning model is adopted to identify and extract the disease target from the road image, and the extracted disease target is classified and subjected to parameter statistics. From this, through can carrying out multilane parallel detection, automatic identification, categorised and the damaged information of record road surface realize going the automation of road surface video and image data that road surface disease detection vehicle gathered to high speed and intelligent analysis, have automatic patrol and examine, intelligent patrol and examine, have with low costs, efficient, automatic, intelligent, the advantage of security, not only be applicable to the daily maintenance of road and patrol and examine, but also can carry out preliminary assessment to road surface quality of use, construction quality and current road surface destruction degree.
In order to achieve the above object, a second embodiment of the present invention provides a road detection device, including: the acquisition module is used for acquiring a road image; the recognition and extraction module is used for recognizing and extracting the disease target of the road image by adopting a deep learning model; and the statistical module is used for classifying the extracted disease targets and carrying out parameter statistics.
According to one embodiment of the invention, the deep learning model is a multi-layer convolutional neural network model.
According to an embodiment of the present invention, the obtaining module is specifically configured to: and acquiring road images of a plurality of lanes continuously acquired by the area-array camera.
According to one embodiment of the invention, the area-array camera is arranged obliquely to the road.
According to an embodiment of the present invention, the obtaining module is further configured to: and carrying out image stabilization processing on the road images of the plurality of lanes.
According to an embodiment of the present invention, the obtaining module is further configured to: acquiring the speed of a road detection vehicle corresponding to the road images of the plurality of lanes; smoothly extracting the road image after image stabilization processing according to the speed of the road detection vehicle; and carrying out image splicing and fusion on the smoothly extracted road image.
According to an embodiment of the present invention, the obtaining module is further configured to: and carrying out image visual transformation on the fused road image to obtain a road image under the aerial view angle.
According to an embodiment of the present invention, the obtaining module is further configured to: and preprocessing the road image under the bird's-eye view angle, wherein the preprocessing comprises gray scale correction and/or image enhancement.
According to the road detection device provided by the embodiment of the invention, the road image can be obtained through the obtaining module, the recognition and extraction module is used for recognizing and extracting the disease target from the road image by adopting the deep learning model, and the statistical module is used for classifying and carrying out parameter statistics on the extracted disease target. From this, through can carrying out multilane parallel detection, automatic identification, categorised and the damaged information of record road surface realize going the automation of road surface video and image data that road surface disease detection vehicle gathered to high speed and intelligent analysis, have automatic patrol and examine, intelligent patrol and examine, have with low costs, efficient, automatic, intelligent, the advantage of security, not only be applicable to the daily maintenance of road and patrol and examine, but also can carry out preliminary assessment to road surface quality of use, construction quality and current road surface destruction degree. In order to achieve the above object, a road detection vehicle according to a third aspect of the present invention includes the above road detection device.
According to the road detection vehicle provided by the embodiment of the invention, through the road detection device, the multi-lane parallel detection can be carried out, the damage information of the road surface can be automatically identified, classified and recorded, the automatic and intelligent analysis of the road surface video and image data collected by the high-speed running road surface disease detection vehicle is realized, the automatic inspection and intelligent inspection are realized, the advantages of low cost, high efficiency, automation, intelligence and safety are realized, and the road detection vehicle is not only suitable for the routine maintenance inspection of the road, but also can be used for preliminarily evaluating the use quality, the construction quality and the damage degree of the existing road surface.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a road detection method according to an embodiment of the invention;
FIG. 2 is a schematic diagram of multi-lane parallel detection according to one embodiment of the present invention;
FIG. 3 is a schematic illustration of a pavement damage identification slip in accordance with one embodiment of the present invention;
FIG. 4 is a schematic diagram of the hardware connections of a road detection system according to one embodiment of the invention;
FIG. 5 is a functional block diagram of a road detection system according to one embodiment of the present invention;
FIG. 6 is a flow diagram of a road detection method according to one embodiment of the invention;
FIG. 7 is a block schematic diagram of a road detection device according to one embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
The following describes a road detection vehicle, a road detection method and a road detection device according to an embodiment of the invention with reference to the accompanying drawings.
Before the road detection method according to the embodiment of the present invention is introduced, a road detection method in the related art will be briefly described.
In the related art, a linear array camera and an auxiliary lighting device are generally used on hardware, the linear array camera and the auxiliary lighting device are installed on a road detection vehicle, a camera is triggered to shoot through an output signal of an encoder installed on a wheel, an industrial control computer is used for image acquisition and storage, and a generator is required to be arranged on the vehicle to supply power to the detection device.
However, in the prior art, the camera is triggered to shoot by installing the wheel encoder to output signals, the wheels need to be modified, the equipment integration level is not high, the installation is inconvenient, the cost is high, only one lane can be detected at a time, the efficiency is low, due to the shooting angle problem, the shadow of the vehicle body can influence the image brightness in the sunlight, auxiliary lighting equipment is needed, the consumed power is high, a generator needs to be equipped, and meanwhile, the exposure needs to be adjusted manually on the vehicle. The generator has large noise and large volume and is not easy to install.
The present invention provides a road detection method based on the above problems.
Fig. 1 is a flowchart of a road detection method according to an embodiment of the present invention. As shown in fig. 1, the road detection method includes the steps of:
and S1, acquiring a road image.
According to one embodiment of the present invention, acquiring a road image includes: and acquiring road images of a plurality of lanes continuously acquired by the area-array camera.
Wherein, according to one embodiment of the invention, the area-array camera is arranged obliquely to the road.
Specifically, as shown in fig. 2, the embodiment of the invention can implement parallel detection of road images of multiple lanes by arranging the area-array camera and the road in an inclined manner, thereby greatly improving the working efficiency and reducing the interference of the shadow of the vehicle body under sunlight to the images.
That is, the invention can shoot from different angles, realize the simultaneous scanning of multiple lanes, and to the road surface of two lanes of one-way, can change the lane to go at will while the road inspection car patrols and examines; preferably, for a road surface with three lanes, the road inspection vehicle can be in the middle lane during inspection. According to an embodiment of the present invention, acquiring a road image further includes: the image stabilization processing is performed on road images of a plurality of lanes.
It should be understood that shake often occurs during normal shooting, resulting in unclear captured images, and therefore, image stabilization processing can be performed on a plurality of acquired road images through an anti-shake technique in the related art, thereby improving processing efficiency.
According to an embodiment of the present invention, acquiring a road image further includes: acquiring the speed of a road detection vehicle corresponding to road images of a plurality of lanes; smoothly extracting the road image after the image stabilization processing according to the speed of the road detection vehicle; and carrying out image splicing and fusion on the smoothly extracted road image.
Specifically, the embodiment of the invention can be used for preprocessing the continuously shot image data, extracting effective information and improving the storage, processing and analysis efficiency of the data. Due to the fact that the data volume of the road image is large, the deep learning algorithm is extremely complex, and after the data are shot, the data need to be preprocessed to obtain an analysis result as soon as possible.
Therefore, the embodiment of the invention estimates the speed information from the continuously shot images by developing a speed matching video compression algorithm, uses a continuous smooth compilation driving support system, and smoothly extracts and compresses the video data according to the speed, so that the storage, processing and analysis efficiency of the data is improved by about 30%.
According to an embodiment of the present invention, acquiring a road image further includes: and carrying out image visual transformation on the fused road image to obtain a road image under the aerial view angle.
According to an embodiment of the present invention, acquiring a road image further includes: and preprocessing the road image under the bird's-eye view angle, wherein the preprocessing comprises gray scale correction and/or image enhancement.
Specifically, the embodiment of the invention can realize the adaptation of different visual angles through an image visual angle transformation algorithm, and simultaneously can avoid the occurrence of vehicle body shadows under sunlight in a photo, reduce the illumination requirement and save the electric power and the cost.
And S2, recognizing and extracting the disease target of the road image by adopting a deep learning model.
Optionally, according to an embodiment of the invention, the deep learning model is a multi-layer convolutional neural network model.
Specifically, road diseases mainly include road cracks, road pits, asphalt road flooding oil and the like, and the embodiment of the invention can identify and extract disease targets through deep learning model road images. The multilayer convolutional neural network model can extract the characteristics of a target through conduction and characteristic transformation of a plurality of levels and can quickly locate an interest region in a picture; the multilayer convolutional neural network model can self-regulate parameters and weights in deep learning and training, so as to achieve more and more accurate recognition effect; meanwhile, because the multilayer convolutional neural network requires large-scale operation, the acquired data generally cannot be processed in real time, and the geographical position information can be positioned and recorded through a GNSS (Global Navigation Satellite System) device.
For example, taking a hole as an example, the embodiment of the present invention may train a marked road damage picture sample in advance through a deep learning model, and when a road image is obtained as shown in fig. 3, a damage in the map may be marked and a damaged area ratio may be output.
And S3, classifying the extracted disease targets and carrying out parameter statistics.
It can be understood that after the deep learning model is adopted to identify and extract the disease target from the road image, the damage of the road can be identified and the damaged area proportion is output, and the extracted disease target is classified and subjected to parameter statistics, so that the inspection and the maintenance of related technicians are greatly facilitated.
For those skilled in the art to further understand the road detection method of the embodiment of the present invention, a detailed description is provided below with an embodiment.
Specifically, the detection system related to the road detection method of the embodiment of the invention mainly comprises two parts: a vehicle-mounted part and a background part. The vehicle-mounted part can be a road detection vehicle, such as a road detection vehicle for collecting road video data and position information, preprocessing the data and storing the data. And analyzing the acquired data through the background part, quickly identifying various road surface damages, carrying out classification statistics and recording the geographic position.
The hardware connection diagram of the road detection system can be as shown in fig. 4, wherein the vehicle-mounted part is hardware mounted on a road detection vehicle, road images can be acquired through a camera array, geographical position information is positioned and recorded through GNSS positioning equipment, data storage is carried out through a vehicle-mounted computer, and data image preprocessing and intelligent pavement damage identification are carried out through a background server. As shown in fig. 5, fig. 5 is a block diagram of a system functional module corresponding to a hardware connection diagram of the road detection system, a camera array is used for video acquisition, GNSS positioning equipment is used for acquiring positioning information and can be selectively installed, an on-board computer is used for data storage, and a background server is used for preprocessing digital images and identifying intelligent road surface diseases.
Specifically, as shown in fig. 6, the above-mentioned road detection method may include the following steps:
s601, continuously collecting road images of a plurality of lanes through an area array camera.
S602, image stabilization processing is performed on road images of a plurality of lanes.
S603, acquiring the speed of a road detection vehicle corresponding to the road images of the plurality of lanes; smoothly extracting the road image after the image stabilization processing according to the speed of the road detection vehicle; and carrying out image splicing and fusion on the smoothly extracted road image.
And S604, carrying out image visual transformation on the fused road image to obtain a road image under the bird' S-eye view angle.
And S605, preprocessing the road image under the bird' S-eye view angle, wherein the preprocessing comprises gray scale correction and/or image enhancement.
And S606, recognizing and extracting the disease target of the road image by adopting a deep learning model.
And S607, classifying the extracted disease targets and carrying out parameter statistics.
That is, the road detection method of the embodiment of the present invention is a significant improvement over the road detection method in the related art. Among them, the comparison between the embodiment of the present invention and the related art can be as shown in table 1.
TABLE 1
According to the road detection method provided by the embodiment of the invention, the road image can be obtained, the deep learning model is adopted to identify and extract the disease target from the road image, and the extracted disease target is classified and subjected to parameter statistics. From this, through can carrying out multilane parallel detection, automatic identification, categorised and the damaged information of record road surface realize going the automation of road surface video and image data that road surface disease detection vehicle gathered to high speed and intelligent analysis, have automatic patrol and examine, intelligent patrol and examine, have with low costs, efficient, automatic, intelligent, the advantage of security, not only be applicable to the daily maintenance of road and patrol and examine, but also can carry out preliminary assessment to road surface quality of use, construction quality and current road surface destruction degree.
Fig. 7 is a block diagram schematically illustrating a road detection device according to an embodiment of the present invention. As shown in fig. 7, the road detection device includes: an acquisition module 100, an identification extraction module 200 and a statistics module 300.
The acquiring module 100 is used for acquiring a road image. The identification and extraction module 200 is used for identifying and extracting the disease target of the road image by adopting a deep learning model. The statistical module 300 is used for classifying the extracted disease targets and performing parameter statistics.
According to one embodiment of the invention, the deep learning model is a multi-layer convolutional neural network model.
According to an embodiment of the present invention, the obtaining module 100 is specifically configured to: and acquiring road images of a plurality of lanes continuously acquired by the area-array camera.
According to one embodiment of the invention, the area-array camera is arranged obliquely to the road.
According to an embodiment of the present invention, the obtaining module 100 is further configured to: the image stabilization processing is performed on road images of a plurality of lanes.
According to an embodiment of the present invention, the obtaining module 100 is further configured to: acquiring the speed of a road detection vehicle corresponding to road images of a plurality of lanes; smoothly extracting the road image after the image stabilization processing according to the speed of the road detection vehicle; and carrying out image splicing and fusion on the smoothly extracted road image.
According to an embodiment of the present invention, the obtaining module 100 is further configured to: and carrying out image visual transformation on the fused road image to obtain a road image under the aerial view angle.
According to an embodiment of the present invention, the obtaining module 100 is further configured to: and preprocessing the road image under the bird's-eye view angle, wherein the preprocessing comprises gray scale correction and/or image enhancement.
It should be noted that the explanation of the embodiment of the road detection method is also applicable to the road detection device of the embodiment, and is not repeated herein.
According to the road detection device provided by the embodiment of the invention, the road image can be obtained through the obtaining module, the recognition and extraction module is used for recognizing and extracting the disease target from the road image by adopting the deep learning model, and the statistical module is used for classifying and carrying out parameter statistics on the extracted disease target. From this, through can carrying out multilane parallel detection, automatic identification, categorised and the damaged information of record road surface realize going the automation of road surface video and image data that road surface disease detection vehicle gathered to high speed and intelligent analysis, have automatic patrol and examine, intelligent patrol and examine, have with low costs, efficient, automatic, intelligent, the advantage of security, not only be applicable to the daily maintenance of road and patrol and examine, but also can carry out preliminary assessment to road surface quality of use, construction quality and current road surface destruction degree.
The embodiment of the invention provides a road detection vehicle, which comprises the road detection device.
According to the road detection device provided by the embodiment of the invention, the multi-lane parallel detection can be carried out, the damage information of the road surface can be automatically identified, classified and recorded, the automatic and intelligent analysis of the road surface video and image data collected by the high-speed running road surface disease detection vehicle is realized, the automatic inspection and the intelligent inspection are realized, the advantages of low cost, high efficiency, automation, intelligence and safety are realized, the road detection device is not only suitable for the daily maintenance inspection of the road, but also can be used for preliminarily evaluating the use quality, the construction quality and the damage degree of the existing road surface.
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" 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.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean 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.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A road detection method, comprising:
acquiring a road image;
recognizing and extracting a disease target of the road image by adopting a deep learning model;
and classifying the extracted disease targets and carrying out parameter statistics.
2. The road detection method of claim 1, wherein the deep learning model is a multilayer convolutional neural network model.
3. The road detection method of claim 1, wherein the acquiring of the road image comprises:
and acquiring road images of a plurality of lanes continuously acquired by the area-array camera.
4. The road detection method according to claim 3, wherein the area-array camera is disposed obliquely to the road.
5. The road detection method of claim 4, wherein the acquiring of the road image further comprises:
and carrying out image stabilization processing on the road images of the plurality of lanes.
6. The road detection method of claim 5, wherein the acquiring a road image, further comprises:
acquiring the speed of a road detection vehicle corresponding to the road images of the plurality of lanes;
smoothly extracting the road image after image stabilization processing according to the speed of the road detection vehicle;
and carrying out image splicing and fusion on the smoothly extracted road image.
7. The road detection method of claim 6, wherein the acquiring a road image, further comprises:
and carrying out image visual transformation on the fused road image to obtain a road image under the aerial view angle.
8. The road detection method of claim 7, wherein the acquiring a road image further comprises:
and preprocessing the road image under the bird's-eye view angle, wherein the preprocessing comprises gray scale correction and/or image enhancement.
9. A road detection device, comprising:
the acquisition module is used for acquiring a road image;
the recognition and extraction module is used for recognizing and extracting the disease target of the road image by adopting a deep learning model;
and the statistical module is used for classifying the extracted disease targets and carrying out parameter statistics.
10. A road detection vehicle, comprising: the road detection device of claim 8.
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