CN112395985B - Ground unmanned vehicle vision road detection method based on unmanned aerial vehicle image - Google Patents
Ground unmanned vehicle vision road detection method based on unmanned aerial vehicle image Download PDFInfo
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Abstract
The invention discloses a ground unmanned vehicle vision road detection method based on unmanned aerial vehicle images, which comprises the steps of firstly training two road detection models respectively used for identifying road areas in aerial images and vehicle-mounted images; then, respectively obtaining common interest area images of the aerial image and the vehicle-mounted image by using a color particle tracking algorithm, a direction matching method based on the straight line segments and inverse projection transformation; and respectively carrying out road detection on the two common interest area images, and carrying out weighted average on the identification results to obtain a road detection result with fused overlooking visual angles. The unmanned aerial vehicle is used for expanding the view field, the perception range of the unmanned vehicle is expanded, the road detection is carried out by combining the overlooking visual angle information, the accuracy of the detection of the distant road part is effectively improved, and the robustness of the road detection is improved.
Description
Technical Field
The invention belongs to a road detection technology based on vision, and particularly relates to a ground unmanned vehicle vision road detection method based on an unmanned aerial vehicle image.
Background
One of the bases on which a ground unmanned vehicle can autonomously travel is to accurately sense the surrounding environment, with road region information being the most basic. The traditional image analysis method and the learning-based method can effectively distinguish road areas, but the vehicle-mounted camera has limited erection height, so that the perception range of an unmanned vehicle is limited to a great extent.
Disclosure of Invention
The invention aims to provide a ground unmanned vehicle visual road detection method based on an unmanned aerial vehicle image.
The technical scheme for realizing the purpose of the invention is as follows: the utility model provides an utilize camera that unmanned aerial vehicle carried on to increase road image information of visual angle of looking down on a large scale for unmanned aerial vehicle, includes the following step:
step 1, respectively extracting superpixel characteristics of a vehicle-mounted road image and an aerial image, and manufacturing a vehicle-mounted road image detection model training set and an aerial image road detection model training set;
step 2, training an SVM (support vector machine) model by utilizing a vehicle-mounted road image detection model training set and an aerial image road detection model training set to respectively obtain a vehicle-mounted image road detection model and an aerial image road detection model;
step 3, setting common interest areas of the unmanned vehicle and the unmanned aerial vehicle, and respectively generating corresponding common interest area images in the original vehicle-mounted road image and the aerial photographing road image;
and 4, carrying out road detection and image fusion on the common interest area image to obtain a fused road detection result image.
Preferably, the specific method for making the training set of the vehicle-mounted road image detection model and the training set of the aerial image road detection model comprises the following steps:
segmenting the vehicle-mounted road image and the aerial photographing road image into superpixel units by using linear iterative clustering;
constructing a feature vector by taking a super pixel as a unit;
marking the road super pixel as 1 and the non-road super pixel as 0;
and forming training data of a training set by the feature vectors of the super pixels and the corresponding label information.
Preferably, color and texture feature vectors are constructed by taking the super-pixels as units, and the color feature vectors are obtained from a gray level histogram of the super-pixels; the texture feature vector is obtained by a gray level histogram of the super pixel unit after filtering through a Laplacian operator and a Prewitt operator.
Preferably, the SVM model uses a Gaussian kernel as a kernel function.
Preferably, the specific method for generating the corresponding common interest area images in the original vehicle-mounted road image and the aerial road image respectively is as follows:
determining the range of the road area concerned commonly by the aerial image of the road by adopting a color particle filter algorithm and a direction matching method based on a straight line segment to obtain an image of the common interest area;
and (4) carrying out inverse projection transformation on the vehicle-mounted road image to obtain a common interest area image.
Compared with the prior art, the invention has the following remarkable advantages: the invention utilizes the characteristic of large observation range of the unmanned aerial vehicle, adopts the unmanned aerial vehicle to expand the visual field, and combines the overlooking visual angle information to detect the road, thereby effectively improving the accuracy of the detection of the distant road part.
Drawings
Fig. 1 is a schematic view of an unmanned aerial vehicle and an unmanned vehicle.
FIG. 2 is a flow chart of the present invention.
FIG. 3 is a captured original image, and FIGS. 3 (a) (b) are aerial images; fig. 3 (c) and (d) are vehicle-mounted views.
Fig. 4 is a diagram showing the result of super-pixel division, and (a) to (d) in fig. 4 are corresponding diagrams showing the result of super-pixel division in (a) to (d) in fig. 3.
Fig. 5 is a pose information extraction diagram of a vehicle target in an aerial image, fig. 5 (a) is a vehicle target image detected by particle filtering, fig. 5 (b) is an edge detection diagram of a target vehicle, and fig. 5 (c) is a cumulative probability hough transform result diagram.
Fig. 6 is a common region of interest image of the aerial image, and fig. 6 (a) and 6 (b) are the common region of interest images of fig. 3 (a) and 3 (b), respectively.
Fig. 7 is a common region of interest map of the on-vehicle image, and fig. 7 (a) and 7 (b) are the common region of interest images of fig. 3 (c) and 3 (d), respectively.
Fig. 8 is a road detection result image of a common interest area of the vehicle-mounted image and the aerial image. Fig. 8 (a), 8 (b) are road detection results of common interest areas of the aerial images, and fig. 8 (c), 8 (d) are road detection results of common interest areas of the on-vehicle images.
Fig. 9 is a fused road detection result image.
Detailed Description
As shown in fig. 1 and 2, a ground unmanned vehicle vision road detection method based on unmanned aerial vehicle images comprises the following specific steps:
step 1, respectively extracting superpixel characteristics from the vehicle-mounted road image and the aerial image, and making a training set, namely making a vehicle-mounted road image detection model training set and an aerial image road detection model training set, wherein the specific method comprises the following steps:
dividing the vehicle-mounted road image and the aerial photographing road image into super pixel units by using linear iterative clustering (SLIC), and establishing a color and texture histogram for each super pixel to serve as a super pixel feature; and marking the road superpixel as 1 and the non-road superpixel as 0 in a manual marking mode. And combining the super-pixel characteristics and corresponding labeling information to form a vehicle-mounted road image detection model training set and an aerial image road detection model training set.
In one embodiment, for each frame of original image, a slic function in python is called to divide the image into 70 superpixel units, and a feature vector is constructed in the unit of superpixel. The feature vector used by the invention comprises a color part and a texture part, and the color feature is obtained by a gray level histogram of the super pixel; the texture features are obtained by the gray level histogram of the super pixel unit after filtering through the Laplacian operator and the Prewitt operator. The class label of each super-pixel is determined by the proportion of road pixels therein. The road pixel proportion is greater than 50% of the superpixels, and is marked as 1, otherwise, the road pixel proportion is marked as 0. And forming training data of a training set by the feature vectors of the super pixels and corresponding labeling information.
And 2, respectively training Support Vector Machine (SVM) models by using the training sets obtained in the step 1 to respectively obtain a vehicle-mounted image road detection model and an aerial image road detection model, wherein the SVM models use Gaussian kernels as kernel functions.
Step 3, as shown in fig. 3-7, setting common interest areas of the unmanned vehicle and the unmanned aerial vehicle, and respectively generating corresponding common interest area images in the original vehicle-mounted road image and the aerial photographing road image;
specifically, the area with the width of 25m and the length of 25m in front of the head of the unmanned vehicle under the overlooking visual angle is selected as the common interest area. The unmanned aerial vehicle aerial road image estimates vehicle position and attitude information through a particle filter algorithm and a straight line segment matching algorithm to generate a corresponding common interest area image; the vehicle-mounted road image is converted into an overlooking visual angle image through inverse projection transformation, and then a common interest area image is generated.
In a further embodiment, a color particle filter algorithm and a direction matching method based on straight line segments are adopted for the aerial road image to determine the common concerned road area range, so as to obtain a common interest area image, and the specific method is as follows:
the method comprises the following steps of determining the position information of a vehicle in an aerial image through a particle filter algorithm based on a color histogram, wherein the method comprises the following specific steps:
a) Manually marking a vehicle target area of an initial frame, and calculating the histogram color distribution of the area;
b) Setting the particle model to S = { x, y, v = { (x, y, v) } x ，v y ，H x ，H y A, where x, y are the particle center positions, v x ，v y Is the speed of movement of the particles in the x, y directions, H x ，H y Representing the width and height of the region in which the particle is located, and a is the corresponding scale factor. Set of particle samples S for a given time t-1 t-1 The initial weight of the particles is 1/N, and N is the sample set size. The vehicle localization algorithm for processing each frame of image is as described in steps c) -g):
c) For particle set S t-1 Resampling is performed. At S t-1 According to the weight
Selecting N samples, specifically: calculating a normalized cumulative probability for a weight array
Represents a set S of particles t-1 The weight value of the ith particle in (c),
representing the cumulative probability of the first k particles of the current image. Generating N [0,1]]At intervalsRandom variables meeting the uniform distribution form an array r; for each element r [ i ] in the array r]Searching normalized cumulative probability array c t-1 When it is satisfied
When j [ i ] is recorded]K (= k). Finally obtaining an array j containing N particle element indexes, wherein all elements in the j meet the requirement
Updating a set of particle samples
d) According to the system state equation S t ＝AS t-1 +w t-1 Calculating a new set S of particles t And estimating the position of the new particle. Where a is the likelihood match values for the N particles with respect to the initial state color histogram and w is gaussian noise.
e) For set S t Is calculated for each particle and compared to the initial vehicle target area color histogram. Updating estimation to obtain a new weight probability, specifically: for state set S t Calculates a color histogram distribution as:
wherein the normalization factor
k is a kernel density function, scale factor
Delta is the Kronecker trigonometric function, h (X) i ) Is X i The pixel value of (d); bin values of the color histogram. Calculating Bhattacharyya coefficient of each particle color distribution and target model color distribution
Rho is a similarity measurement function, and p and q are particles respectively
Color distribution of the model and the initial target model, and calculating respective weight values according to the coefficients
Where σ is the variance of the gaussian function.
f) According to the weight
Estimate set S t Extracting the position coordinates as a vehicle target tracking output:
wherein the vector E [ S t ]Containing the position information x, y of the vehicle target in the image at the t moment, and the width and height information H of the current vehicle target detection window x ,H y 。
g) Calculating the color distribution of the target histogram in the detection window obtained in the previous step as the color distribution of the vehicle target of the next frame, calculating the Bhattacharyya coefficient of the current target color distribution and the previous color distribution as the weight of the next frame
The position coordinates of the unmanned vehicle in each frame of image of the image sequence and the size of a detection window on the image, namely the position information of the vehicle are obtained by the algorithm steps a) -g).
Secondly, acquiring the attitude information of the vehicle by using a direction matching method based on straight line segments for the vehicle target image acquired by the method, wherein the specific method comprises the following steps:
and (5) using a canny operator for the vehicle target image to obtain an edge information image of the vehicle. And calling a HoughLinesP function in OpenCV to calculate cumulative probability Hough transformation for the edge image, and extracting all line segments meeting the conditions. And detecting a straight line segment with the minimum straight line length of one half of the image width and the maximum straight line gap of the image width. And calculating the rotation angle of the straight line section relative to the horizontal or vertical direction to obtain the deflection angle of the target vehicle, namely the attitude information of the vehicle.
And finally, selecting a corresponding area in front of the aerial image vehicle for rotary stretching according to the vehicle position information and the vehicle posture information to obtain a common interest area image in the aerial image.
In a further embodiment, the specific method for obtaining the common interest area image by using the inverse projection transformation to the vehicle-mounted road image comprises the following steps:
and converting the original vehicle-mounted image into a depression image by adopting inverse projection transformation. The transformation from the image coordinate system [ U, V ] to the real world coordinate system [ X, Y, Z ] is as follows:
k is a 3 x 3 camera internal reference matrix, R and T are camera external references, wherein R represents a 3 x 3 rotation matrix, T is a 3 x 1 translation vector, and S is a coefficient constant. Assuming that the ground is a Z =0 road surface, K · [ R | T ] can be reduced from 3 × 4 columns to a matrix of 3 × 3 columns, let H = K · [ R | T ], and there are:
the camera is calibrated to obtain the internal and external parameters K, R and T of the camera, and the conversion matrix H can be obtained by substituting the parameters into the formula (1), so that the one-to-one correspondence relationship from the points in the image plane to the points in the road surface is obtained. The original image is converted into an inverse projective transformation image by matrix transformation. And selecting a corresponding area in front of the vehicle in the vehicle-mounted road image to carry out rotary stretching to obtain a common interest area image of the vehicle-mounted road image.
Step 4, as shown in fig. 8 and 9, performing road detection on the common interest area image under the double-view angle obtained in step 3, and fusing two road detection result images to obtain a final road detection result image, wherein the specific steps are as follows:
and (4) respectively inputting the common interest area images under the double view angles obtained in the step (3) into the vehicle-mounted image road detection model and the aerial image road detection model in the step (2) to obtain two road prediction binary images. Carrying out weighted average on the two binary images, wherein the fusion strategy is as follows:
R＝α×G+(1-α)×A
g and A are output result images of the vehicle-mounted image road detection model and the aerial image road detection model respectively, alpha is an image fusion weight, and alpha is 0.5. And R is the road detection result image finally obtained.
Claims (10)
1. A ground unmanned vehicle vision road detection method based on unmanned aerial vehicle images is characterized by comprising the following steps:
step 1, respectively extracting superpixel characteristics of a vehicle-mounted road image and an aerial image, and manufacturing a vehicle-mounted road image detection model training set and an aerial image road detection model training set;
step 2, training an SVM model by utilizing a vehicle-mounted road image detection model training set and an aerial image road detection model training set to respectively obtain a vehicle-mounted image road detection model and an aerial image road detection model;
step 3, setting common interest areas of the unmanned vehicle and the unmanned aerial vehicle, and respectively generating corresponding common interest area images in the original vehicle-mounted road image and the aerial photographing road image;
and 4, carrying out road detection and image fusion on the common interest area image to obtain a fused road detection result image.
2. The ground unmanned vehicle vision road detection method based on unmanned aerial vehicle images as claimed in claim 1, wherein the specific method for making the vehicle-mounted road image detection model training set and the aerial image road detection model training set is as follows:
segmenting the vehicle-mounted road image and the aerial photographing road image into superpixel units by using linear iterative clustering;
constructing a feature vector by taking a super pixel as a unit;
marking the road super pixel as 1 and the non-road super pixel as 0;
and forming training data of a training set by the feature vectors of the super pixels and the corresponding label information.
3. The ground unmanned aerial vehicle visual road detection method based on the unmanned aerial vehicle image as claimed in claim 2, wherein color and texture feature vectors are constructed by taking a super pixel as a unit, and the color feature vectors are obtained from a gray histogram of the super pixel; the texture feature vector is obtained by a gray level histogram of the super pixel unit after filtering through a Laplacian operator and a Prewitt operator.
4. The method of claim 1, wherein the SVM model uses a Gaussian kernel as a kernel function.
5. The ground unmanned vehicle visual road detection method based on unmanned aerial vehicle images as claimed in claim 1, wherein the specific method for respectively generating corresponding common interest area images in the original vehicle-mounted road image and the aerial road image is as follows:
determining the range of the road area concerned commonly by the aerial image of the road by adopting a color particle filter algorithm and a direction matching method based on a straight line segment to obtain an image of the common interest area;
and (4) carrying out inverse projection transformation on the vehicle-mounted road image to obtain a common interest area image.
6. The ground unmanned vehicle visual road detection method based on unmanned aerial vehicle images as claimed in any one of claims 1-5, wherein a specific method for generating corresponding common interest area images in the aerial photography road images is as follows:
marking a vehicle target area of an initial frame, and calculating the histogram color distribution of the area;
setting the particle model to S = { x, y, v = { (x, y, v) } x ,v y ,H x ,H y A } where x, y are the particle center positions, v x ,v y Is the speed of movement of the particles in the x, y directions, H x ,H y Representing the width and height of the region in which the particle is located, a being the corresponding scale factor; set of particle samples S for a given time t-1 t-1 The initial weight of the particles is 1/N, and N is the size of the sample set;
for particle set S t-1 Resampling is carried out;
according to the system state equation S t ＝AS t-1 +w t-1 Calculating a new set S of particles t Estimating the positions of new particles, wherein A is the likelihood matching values of N particles relative to the initial state color histogram, and w is Gaussian noise;
for set S t Calculating a color histogram of each particle, comparing the color histogram with an initial color histogram of the target area of the vehicle, updating the estimation, and obtaining a new weight probability
Estimating set S according to new weight probability t Extracting the position coordinates as a vehicle target tracking output:
wherein the vector E [ S ] t ]Containing the position information x, y of the vehicle target in the image at the t moment, and the width and height information H of the current vehicle target detection window x ,H y ；
Calculating the color distribution of the target histogram in the detection window as the color distribution of the next frame vehicle target, and calculating the color distribution of the current targetBhattacharyya coefficient of the color distribution and the previous color distribution as the weight of the next frame
7. A ground unmanned vehicle vision road detection method based on unmanned aerial vehicle image as claimed in claim 6, characterized in that, the particle set S t-1 The specific method for resampling comprises the following steps:
calculating a normalized cumulative probability for a weight array
Represents a set S of particles t-1 The weight value of the ith particle in (c),
representing the cumulative probability of the first k particles of the current image;
generating N random variables which meet the requirement of uniform distribution in the interval of [0,1] to form an array r;
for each element r [ i ] in the array r]Searching normalized cumulative probability array c t-1 When it is satisfied
Then, record j [ i]K, get an array j containing N particle element indices, all elements in j satisfying
Updating a set of particle samples
8. The method for detecting the visual road of the ground unmanned vehicle based on the unmanned aerial vehicle image as claimed in claim 6, wherein the specific method for obtaining the new weight probability is as follows: for the state set S t Calculating a color histogram distribution for each particle in (1)
Wherein the normalization factor
y is the particle center value, k is the kernel density function, scale factor
Delta is the Kronecker trigonometric function, h (X) i ) Is X i The pixel value of (b), m is the bin value of the color histogram;
calculating Bhattacharyya coefficient of each particle color distribution and target model color distribution
Rho is a similarity measurement function, and p and q are particles respectively
Color distribution of the model and the initial target model, and calculating each weight value according to the coefficient
Where σ is the variance of the gaussian function.
9. The ground unmanned vehicle vision road detection method based on unmanned aerial vehicle images as claimed in claim 1, wherein for vehicle-mounted road images, the specific method for obtaining common interest area images by using inverse projection transformation is as follows:
the original vehicle-mounted image is converted into a depression image by adopting inverse projection transformation, and the transformation relation from an image coordinate system [ U, V ] to an actual world coordinate system [ X, Y, Z ] is as follows:
k is a camera internal reference matrix, and R and T are camera external references;
converting the original image into an inverse projection conversion image through matrix conversion; and selecting a corresponding area in front of the vehicle in the vehicle-mounted road image to carry out rotary stretching to obtain a common interest area image of the vehicle-mounted road image.
10. The ground unmanned vehicle vision road detection method based on unmanned aerial vehicle images as claimed in claim 1, wherein road detection and image fusion are performed on common interest area images, and a specific method for obtaining a fused road detection result image is as follows:
respectively inputting the common interest area images under the double view angles into the vehicle-mounted image road detection model and the aerial image road detection model in the step 2 to obtain two road prediction binary images;
carrying out weighted average on the two binary images, wherein the fusion strategy is as follows:
R＝α×G+(1-α)×A
wherein G and A are respectively output result images of the vehicle-mounted image road detection model and the aerial image road detection model, alpha is an image fusion weight, alpha is 0.5, and R is a road detection result image finally obtained.
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