CN113139975A - Road characteristic-based road surface segmentation method and device - Google Patents
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Abstract
The embodiment of the disclosure provides a road surface segmentation method, a road surface segmentation device, road surface segmentation equipment and a computer-readable storage medium. The method comprises the steps of obtaining a road image, and selecting a road sample region from the road image; classifying the road sample region to obtain a road sample category data set; segmenting the road image according to the road sample category data set to obtain a preliminary road segmentation result; and processing the preliminary road segmentation result to obtain a final road segmentation result. In this way, accurate road segmentation can be performed in a complex road environment.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of road segmentation, and more particularly, to a road surface segmentation method, apparatus, device, and computer-readable storage medium based on road characteristics.
Background
Segmentation of road images is one of key technologies in the fields of automatic driving, road asset management, road technical condition management, and the like. The purpose of road segmentation is to distinguish between "roads" and "non-roads".
The traditional road segmentation method mainly comprises clustering, threshold segmentation, region growing and other methods based on the traditional image processing method, and a road segmentation algorithm based on deep learning. Under a complex road environment (such as a shadow road, a ponding road, a road and surrounding buildings which are connected into a whole), the method/algorithm has the problems of missing extracted road boundaries, non-road areas made of the same material in the road and the like, and further influences subsequent service application based on a road segmentation result.
Disclosure of Invention
According to an embodiment of the present disclosure, a road surface segmentation scheme based on road characteristics is provided.
In a first aspect of the disclosure, a road surface segmentation method based on road characteristics is provided. The method comprises the following steps:
acquiring a road image, and selecting a road sample area from the road image;
classifying the road sample region to obtain a road sample category data set;
segmenting the road image according to the road sample category data set to obtain a preliminary road segmentation result;
and processing the preliminary road segmentation result to obtain a final road segmentation result.
Further, the classifying the road sample region to obtain a road sample category data set includes:
establishing an RGB three-dimensional space;
drawing the road sample region in the RGB three-dimensional stereo space according to the RGB three-channel values of the road sample region;
counting the occurrence frequency of the pixels of the road sample area in each square in the RGB three-dimensional space, sequencing the pixels from high to low, and selecting the pixels with the occurrence frequency larger than a first threshold value to participate in road sample classification;
and classifying the road samples according to the difference value of the channel value of each road sample and the Euclidean distance to obtain a road sample category data set.
Further, the segmenting the road image according to the road sample category data set to obtain a preliminary road segmentation result includes:
calculating the RGB difference value and Euclidean distance between the road image and the sample category data set;
and performing road segmentation according to the RGB difference and the Euclidean distance to obtain a primary road segmentation result.
Further, the road segmentation according to the RGB difference and the euclidean distance to obtain a preliminary road segmentation result includes:
performing road segmentation through pixel-by-pixel operation according to the RGB difference value and the Euclidean distance, and if the RGB difference value is smaller than a difference threshold value and the Euclidean distance is smaller than a distance threshold value, calibrating the road as a road surface; and if the RGB difference value is greater than or equal to the difference threshold value and the Euclidean distance is greater than or equal to the distance threshold value, the road surface is marked as the non-road surface.
Further, the processing the preliminary road segmentation result to obtain a final road segmentation result includes:
determining a road surface pattern spot of the road according to the preliminary road segmentation result;
determining four boundary points of the road surface according to the road surface pattern spots of the road;
determining the road surface edge according to the four boundary points of the road surface:
performing edge fitting on the edge points in the road surface edge to obtain a final road surface edge;
and obtaining a final road segmentation result according to the final road surface edge.
Further, the determining the road surface edge according to the four boundary points of the road surface includes:
determining a leftmost pixel point set between two boundary points on the left side and a rightmost pixel point set between two boundary points on the right side according to the four boundary points of the road surface;
obtaining a first road edge according to the four boundary points, the leftmost pixel point set and the rightmost pixel point set of the road;
and carrying out slope screening on edge points in the first road surface edge to obtain the road surface edge.
Further, the slope screening of the edge point in the first road surface edge to obtain the road surface edge includes:
calculating the slope between two adjacent edge points in the leftmost pixel point set to obtain the slope value of the edge point with the highest slope frequency on the left side;
calculating the slope between two adjacent edge points in the rightmost pixel point set to obtain the slope value of the edge point with the highest right slope frequency;
and removing the point, in the leftmost pixel point set, of which the edge point slope difference with the highest frequency of the left slope is greater than a second threshold value, and the point, in the rightmost pixel point set, of which the edge point slope difference with the highest frequency of the right slope is greater than the second threshold value, from the first road surface edge to obtain the road surface edge.
In a second aspect of the present disclosure, a road surface segmentation apparatus based on road characteristics is provided. The device includes:
the acquisition module is used for acquiring a road image and selecting a road sample area;
the classification module is used for classifying the road sample regions to obtain a road sample class data set;
the first segmentation module is used for segmenting the road image according to the road sample class data set to obtain a preliminary road segmentation result;
and the second segmentation module is used for processing the preliminary road segmentation result to obtain a final road segmentation result.
In a third aspect of the disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, a computer readable storage medium is provided, having stored thereon a computer program, which when executed by a processor, implements a method as in accordance with the first aspect of the present disclosure.
According to the road surface segmentation method based on the road characteristics, the road sample area is selected by acquiring the road image; classifying the road sample region to obtain a road sample category data set; segmenting the road image according to the road sample category data set to obtain a preliminary road segmentation result; and processing the preliminary road segmentation result to obtain a final road segmentation result, and realizing accurate road segmentation under a complex road environment.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 shows a flow diagram of a road surface segmentation method based on road characteristics according to an embodiment of the present disclosure;
FIG. 2 shows a road image schematic according to an embodiment of the disclosure;
FIG. 3 shows a sample region schematic in accordance with an embodiment of the present disclosure;
FIG. 4 shows a schematic diagram of preliminary road segmentation results according to an embodiment of the present disclosure;
FIG. 5 shows a schematic diagram of edge fitting according to an embodiment of the present disclosure;
FIG. 6 shows a schematic diagram of a final road segmentation result according to an embodiment of the present disclosure;
FIG. 7 shows a block diagram of a road surface segmentation method device based on road characteristics according to an embodiment of the present disclosure;
FIG. 8 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
Fig. 1 shows a flow diagram of a road surface segmentation method 100 based on road characteristics according to an embodiment of the disclosure, the method 100 comprising:
and S110, acquiring a road image, and selecting a road sample area from the road image.
In some embodiments, the road image comprises as shown in fig. 2.
Further, the complex road environment includes an environment with complex and varied road surface conditions, such as a shadow road, a ponding road and/or a road integrated with surrounding buildings.
In some embodiments, the region in the road image that is in the middle and near the bottom is selected as the road sample region, as shown in FIG. 3.
Preferably, in the road image, the sample area is selected within the range of 0.35-0.65 (35% -65%) in the transverse direction and 0-0.1 (0-10%) in the vertical direction.
And S120, classifying the road sample region to obtain a road sample class data set.
In some embodiments, the road sample region is classified by a method of establishing an RGB three-dimensional stereo space.
Specifically, an RGB three-dimensional space is established for dividing the sample region and the entire image (road image) to provide a three-dimensional space in which the X, Y, Z axes have values ranging from 1 to 26 and are integers. The selection can be made according to the actual application scene.
And drawing the road sample region in the RGB three-dimensional space according to the RGB three-channel values of the road sample region.
Preferably, in order to reduce the computation amount, the RGB three-channel values of the road sample region may be divided by 10 and rounded, and the road sample region may be drawn in the RGB three-dimensional stereo space according to the rounded RGB three-channel values.
And counting the occurrence frequency of the pixels in the road sample area in each square in the RGB three-dimensional space, sequencing the pixels from high to low, and selecting the pixels with the occurrence frequency larger than a first threshold value to participate in road sample classification.
Preferably, the first threshold is 90%. That is, the first 90% of the number cumulative sum (frequency of occurrence) samples are selected to participate in the classification operation. Only the pixels with high occurrence frequency are selected as samples for subsequent classification calculation, so that the operation amount can be effectively reduced, and the efficiency is improved.
Classifying the road samples, refining the road sample categories, and distinguishing roads in different environments in the sample area, such as shadow, soil coverage and the like.
In some embodiments, the road samples are classified according to the difference of the channel values of each road sample and the euclidean distance, so as to obtain a road sample category dataset. And if the difference value (the difference value between the RGB channels) of the channel values of the road samples is smaller than the difference threshold value, and the Euclidean distance is smaller than the distance threshold value, classifying the road samples into the same class of samples, and regarding other situations as different classes.
Preferably, the difference threshold is 10; the distance threshold is 2.5.
And S130, segmenting the road image according to the road sample class data set to obtain a preliminary road segmentation result.
In some embodiments, the RGB difference and the euclidean distance between the road image and the sample category data set are calculated, and road segmentation is performed according to the RGB difference and the euclidean distance, so as to obtain a preliminary road segmentation result as shown in fig. 4.
Specifically, road segmentation is carried out through pixel-by-pixel operation according to the RGB difference value and the Euclidean distance, and if the RGB difference value is smaller than a difference threshold value and the Euclidean distance is smaller than a distance threshold value, the road is marked as a road surface; and if the RGB difference value is greater than or equal to the difference threshold value and the Euclidean distance is greater than or equal to the distance threshold value, the road surface is marked as the non-road surface.
Preferably, the difference threshold is 10; the distance threshold is 2.5.
And S140, processing the preliminary road segmentation result to obtain a final road segmentation result.
In some embodiments, the patch Z with the largest area in the preliminary road segmentation result is selected as the road surface patch of the road.
In some embodiments, from the pattern spot Z, four boundary points of the road surface are determined.
Specifically, the row farthest from the bottom of the image (preliminary segmentation image) in the spot Z and the column thereof are selected as the vertex. Finding left and right demarcation points P in the vertex rangeUL、PUR；
Preferably, the distance P is selectedUL、PURPoints which are positioned on the left and the right in the range of 100 rows are respectively used as upper dividing points of the left side and the right side;
with PULAs the starting point, the point of the first column 0 is the lower left corner point, denoted as PLL(ii) a With PURAs a starting point, the point with the first column as the image width value is the lower right corner point and is marked as PLR；
That is, four boundary points of the road surface are determined, which are:
upper left boundary point PUL；
Lower left boundary point PLL；
Upper right boundary point PUR；
Lower right boundary point PLR。
In some embodiments, a road surface edge is determined from the four boundary points.
Specifically, by using the characteristic that the column value of the road edge is the maximum value and the minimum value of the line, the pixel point set measured leftmost between the left edge point PUL and the PLL and the pixel point set measured rightmost between the right edge point PUR and the PLR are searched in a row unit, and an optimized edge B1 (first road edge) is formed. That is, the middle points of the roads in the same row are removed, and only the outermost points are retained.
In some embodiments, B is1Deleting the pixels with larger slope difference.
Specifically, calculating the slope between two adjacent edge points in the leftmost pixel point set to obtain the slope value of the edge point with the highest left slope frequency;
calculating the slope between two adjacent edge points in the rightmost pixel point set to obtain the slope value of the edge point with the highest right slope frequency;
removing the point with the edge point slope difference with the highest frequency of the left slope and larger than a second threshold from the edge of the first road, and removing the point with the edge point slope difference with the highest frequency of the right slope and larger than the second threshold from the edge of the first road, so as to obtain a new edge B2(road surface edge).
Preferably, the second threshold is 0.05.
In some embodiments, for said B2Is edge-fitted to form an edge B as shown in fig. 53. I.e. the left and right edges are marked with B2The middle point is an edge point, primary function fitting and secondary function fitting are respectively carried out, a function with the minimum residual error after fitting is selected as a left edge function and a right edge function, and a new edge B is formed3。
As shown in FIG. 6, with edge B3And performing road segmentation for the road edge to obtain a final road segmentation result.
According to the embodiment of the disclosure, the following technical effects are achieved:
1. the method for establishing the RGB three-dimensional space is adopted for sample region classification, the accuracy of data is reserved, and meanwhile the calculation dimensionality is reduced.
2. According to the complex diversity of the road surface environment, noise points generated by shadows, accumulated water and the like are removed through two methods of edge point selection and slope screening, edge optimization is carried out through a fitting function, real road edge points are reserved, and the influence of the surrounding environment of the road is reduced.
In conclusion, the method has a good effect on extracting the edges of shadow roads, ponding roads, damaged roads and roads communicated with surrounding buildings (roads in a complex environment), and can provide an efficient preprocessing method for follow-up road management, road technical condition assessment, road width, road lane extraction and the like.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 7 shows a block diagram of a road surface segmentation apparatus 700 based on road characteristics according to an embodiment of the present disclosure. As shown in fig. 7, the apparatus 700 includes:
the acquisition module 710 is used for acquiring a road image and selecting a road sample region from the road image;
the classification module 720 is configured to classify the road sample region to obtain a road sample category data set;
the first segmentation module 730 is used for segmenting the road image according to the road sample category data set to obtain a preliminary road segmentation result;
and a second segmentation module 740, configured to process the preliminary road segmentation result to obtain a final road segmentation result.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
FIG. 8 illustrates a schematic block diagram of an electronic device 800 that may be used to implement embodiments of the present disclosure. As shown, device 800 includes a Central Processing Unit (CPU)801 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)802 or loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data required for the operation of the device 800 can also be stored. The CPU801, ROM802, and RAM803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
A number of components in the device 800 are connected to the I/O interface 805, including: an input unit 806, such as a keyboard, a mouse, or the like; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, or the like; and a communication unit 809 such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows the device 800 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The processing unit 801 performs the various methods and processes described above, such as the method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and/or installed onto device 800 via ROM802 and/or communications unit 809. When loaded into RAM803 and executed by CPU801, a computer program may perform one or more of the steps of method 100 described above. Alternatively, in other embodiments, the CPU801 may be configured to perform the method 100 in any other suitable manner (e.g., by way of firmware).
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A road surface segmentation method based on road characteristics is characterized by comprising the following steps:
acquiring a road image, and selecting a road sample area from the road image;
classifying the road sample region to obtain a road sample category data set;
segmenting the road image according to the road sample category data set to obtain a preliminary road segmentation result;
and processing the preliminary road segmentation result to obtain a final road segmentation result.
2. The method of claim 1, wherein the classifying the road sample region, resulting in a road sample category dataset comprises:
establishing an RGB three-dimensional space;
drawing the road sample region in the RGB three-dimensional stereo space according to the RGB three-channel values of the road sample region;
counting the occurrence frequency of the pixels of the road sample area in each square in the RGB three-dimensional space, sequencing the pixels from high to low, and selecting the pixels with the occurrence frequency larger than a first threshold value to participate in road sample classification;
and classifying the road samples according to the difference value of the channel value of each road sample and the Euclidean distance to obtain a road sample category data set.
3. The method of claim 2, wherein the segmenting the road image according to the road sample class dataset to obtain a preliminary road segmentation result comprises:
calculating the RGB difference value and Euclidean distance between the road image and the sample category data set;
and performing road segmentation according to the RGB difference and the Euclidean distance to obtain a primary road segmentation result.
4. The method of claim 3, wherein the performing road segmentation according to the RGB difference and Euclidean distance to obtain a preliminary road segmentation result comprises:
performing road segmentation through pixel-by-pixel operation according to the RGB difference value and the Euclidean distance, and if the RGB difference value is smaller than a difference threshold value and the Euclidean distance is smaller than a distance threshold value, calibrating the road as a road surface; and if the RGB difference value is greater than or equal to the difference threshold value and the Euclidean distance is greater than or equal to the distance threshold value, the road surface is marked as the non-road surface.
5. The method of claim 4, wherein the processing the preliminary road segmentation result to obtain a final road segmentation result comprises:
determining a road surface pattern spot of the road according to the preliminary road segmentation result;
determining four boundary points of the road surface according to the road surface pattern spots of the road;
determining the road surface edge according to the four boundary points of the road surface:
performing edge fitting on the edge points in the road surface edge to obtain a final road surface edge;
and obtaining a final road segmentation result according to the final road surface edge.
6. The method of claim 5, wherein determining the road surface edge from the four boundary points of the road surface comprises:
determining a leftmost pixel point set between two boundary points on the left side and a rightmost pixel point set between two boundary points on the right side according to the four boundary points of the road surface;
obtaining a first road edge according to the four boundary points, the leftmost pixel point set and the rightmost pixel point set of the road;
and carrying out slope screening on edge points in the first road surface edge to obtain the road surface edge.
7. The method of claim 6, wherein the slope screening the edge points in the first road edge to obtain the road edge comprises:
calculating the slope between two adjacent edge points in the leftmost pixel point set to obtain the slope value of the edge point with the highest slope frequency on the left side;
calculating the slope between two adjacent edge points in the rightmost pixel point set to obtain the slope value of the edge point with the highest right slope frequency;
and removing the point, in the leftmost pixel point set, of which the edge point slope difference with the highest frequency with the left slope is greater than a second threshold value, and the point, in the rightmost pixel point set, of which the edge point slope difference with the highest frequency with the right slope is greater than the second threshold value, from the first road edge to obtain the road edge.
8. A road surface segmentation device based on road characteristics, characterized by comprising:
the acquisition module is used for acquiring a road image and selecting a road sample region from the road image;
the classification module is used for classifying the road sample regions to obtain a road sample class data set;
the first segmentation module is used for segmenting the road image according to the road sample class data set to obtain a preliminary road segmentation result;
and the second segmentation module is used for processing the preliminary road segmentation result to obtain a final road segmentation result.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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