CN110991377B - Front mesh identification method of automobile safety auxiliary system based on monocular vision neural network - Google Patents
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Abstract
The invention discloses a method for identifying a front target of an automobile safety auxiliary system based on a radial basis function neural network, which comprises the following steps: acquiring road condition images in front of a vehicle, and carrying out segmentation pretreatment on the road condition images; extracting edges of the preprocessed road condition images, and searching to obtain an interested region; extracting features of the region of interest to obtain edge features and region features corresponding to the region of interest; constructing a radial basis neural network model by taking the edge features and the region features as input layer vectors, and analyzing the input layer vector features in a neural network to obtain output quantity related to a target; according to the output quantity, a corresponding vehicle target is obtained, and the vehicle target is output as a recognition result.
Description
Technical Field
The invention relates to the field of automobile safety auxiliary driving control, in particular to a front destination identification method of an automobile safety auxiliary system based on a monocular vision neural network.
Background
The automobile safety auxiliary driving system (ADAS) is used for rapidly and accurately extracting information such as vehicles or obstacles in front of the system by using radar or machine vision, and can prompt a driver to avoid collision danger or automatically control the vehicles to realize the early warning or collision avoidance function. The system function is not only suitable for expressway driving conditions, but also particularly important for identifying targets such as non-motor vehicles, pedestrians, obstacles and the like in front of an ADAS system and particularly identifying electric bicycles for other driving environments, especially urban road driving environments, because traffic accident rates of the non-motor vehicles (mainly electric bicycles) and the motor vehicles frequently occur and the proportion is large in urban roads.
Currently, there are few studies on classification of motor vehicles and electric bicycles based on a machine vision detection and identification method. Most methods are directed to detection and identification studies of vehicles and are used in auxiliary driving systems. Such as by using linear geometric feature information of the vehicle, symmetry of the vehicle, or computer vision methods employing special hardware such as color CCDs and binocular CCDs, etc. In addition, there are an optical flow-based method, a template matching method, a support vector machine method, a neural network training method, a multi-sensor information fusion method, a vehicle detection and recognition method based on an AdaBoost method and a support vector machine classifier, and a vehicle detection or recognition method based on deep learning and a high-speed-area convolutional neural network. The research method for vehicle detection has the end result that the region of interest of the vehicle is determined, but the region still has the possibility of false detection, and if the detection result in the region of interest can be deeply confirmed, the accuracy of vehicle target identification can be greatly improved, and the false detection rate is reduced to increase the reliability of system identification.
Disclosure of Invention
The invention designs and develops a front mesh identification method of an automobile safety auxiliary system based on a radial basis function neural network, which performs edge extraction and search on preprocessed road condition images to obtain a region of interest as a possible vehicle region, and constructs a radial basis function neural network vehicle identifier by counting edge characteristics and region characteristic parameters of the vehicle to realize classification of the vehicle and the electric bicycle in a detection region.
The technical scheme provided by the invention is as follows:
a method for identifying the front destination of an automobile safety auxiliary system based on a radial basis function neural network, comprising the following steps:
acquiring road condition images in front of a vehicle, and carrying out segmentation pretreatment on the road condition images;
extracting edges of the preprocessed road condition images, and searching to obtain an interested region;
extracting features of the region of interest to obtain edge features and region features corresponding to the region of interest;
constructing a radial basis neural network model by taking the edge features and the region features as input layer vectors, and analyzing the input layer vector features in a neural network to obtain output quantity related to a target;
obtaining a corresponding vehicle target according to the output quantity, and outputting the vehicle target as a recognition result;
wherein the region of interest includes electric bicycles and motor vehicles.
Preferably, the edge features and the region features corresponding to the region of interest include: edge characteristic parameters and region description characteristic parameters formed by independent invariant moment parameters of sub-coefficients of discrete cosine transform.
Preferably, the discrete cosine transform sub-coefficient calculation formula is:
C(k)＝|F(k)|/F(1)；
wherein C (k) is a discrete cosineThe sub-coefficients of the transform are used,k is the number of discrete sub-coefficients, k=1, 2 …; f (k) =x (k) +jy (k); /> j is the imaginary part of the complex plane n=1, 2,3 … N-1; n is the characteristic point variable of the closed edge curve obtained by edge extraction after image segmentation, N is the number of characteristic points of the closed edge curve obtained by edge extraction after image segmentation, and f (m) =x (m) +jy (m); m is more than or equal to 1 and less than or equal to n, and f (m) is a one-dimensional complex sequence.
Preferably, the independent invariant moment parameter calculation formula is:
wherein ,mu, the coordinates of the central point of the region pq The central moment of the area where the binarized image is located; />m 00 Is zero order geometrical moment, m of the area where the binarized image is located 01 、m 10 Is the first-order geometric moment, m, of the area where the binarized image is located pq The geometric moment of the p+q order of the area where the binary image is located, p is the row order of the central moment of the binary image, and q is the column order of the central moment of the binary image.
Preferably, the target recognition characteristic parameters include: region eccentricity, ratio of short and long axes of the region, region area, region perimeter, and region compactness factor.
Preferably, the radial basis function neural network model is a three-layer neural network model:
the first layer is an input layer, and the feature vector is input into the network;
the second layer is a hidden layer which can be completely connected with the input layer, and the hidden layer node selects a Gaussian radial basis function as a transfer function, and the calculation formula is as follows:
in the formula ,||xp -c i I is the European norm, c i Is the center of the Gaussian function, and sigma is the variance of the Gaussian function;
the third layer is an output layer, 2 output quantities are obtained by calculating weights between the hidden layer and the output layer, and a vehicle target is identified; the output of the network can be obtained from the structure of the radial basis function network as:
in the formula ,for the P-th input sample, p=1, 2, …, P is the total number of samples; c i Omega is the center of hidden layer node of network ij For the connection weight from the hidden layer to the output layer, i=1, 2, …, h is the number of nodes of the hidden layer, y j J=1, 2, …, n for the actual output of the j-th output node of the network for the input sample pair;
wherein ,dj Is the expected output value of the sample.
Preferably, the center of the radial basis function neural network is obtained by a K-means clustering algorithm, and the specific process is as follows:
step one, randomly selecting h training samples as a clustering center c i ，i＝1,2,…h；
Step two, calculating the Euclidean distance between each training sample and the clustering center, and according to the Euclidean distanceDistance assigns each training sample to a respective cluster set ψ of input samples p (p=1, 2, …, P);
step three, recalculating each cluster set psi p Average value of training samples to obtain a new clustering center c i ′；
Step four, repeating the step two and the step three until a new clustering center c is obtained i ' the variation is less than a given threshold, c is obtained i ' is the final basis function center of the radial basis function neural network.
Preferably, the basis function variance solving formula is:
wherein ,σi As the basis function variance, c max To select the maximum distance between centers.
Preferably, the connection weight between the hidden layer and the output layer is calculated by a least square method, and the calculation formula is as follows:
the beneficial effects of the invention are that
The invention designs and develops a front target identification method of an automobile safety auxiliary system based on a radial basis function neural network, carries out edge extraction and search on a preprocessed road condition image, obtains a region of interest as a possible existing vehicle region, constructs a radial basis function neural network vehicle identifier by counting edge characteristics and region characteristic parameters of the vehicle, realizes classification of the vehicle and an electric bicycle in a detection region, greatly improves accuracy of vehicle target identification, and reduces false detection rate so as to improve reliability of system identification.
Drawings
Fig. 1 is a road condition image of the front of a vehicle according to the present invention.
Fig. 2 is a schematic diagram of a vehicle searching region of interest according to the present invention.
Fig. 3 is a block diagram of an RBF neural network according to the present invention.
FIG. 4 is a schematic diagram of an error performance curve of an RBF neural network test according to the present invention.
Detailed Description
The present invention is described in further detail below with reference to the drawings to enable those skilled in the art to practice the invention by referring to the description.
As shown in fig. 1, the method for identifying the front mesh of the automobile safety auxiliary system based on the radial basis function neural network provided by the invention comprises the following steps:
acquiring road condition images in front of a vehicle, and carrying out segmentation pretreatment on the road condition images;
extracting edges of the preprocessed road condition images, and searching to obtain an interested region;
as shown in fig. 2, extracting vehicle features from the region of interest to obtain edge features and region features corresponding to the region of interest;
constructing a radial basis neural network model by taking the edge features and the region features as input layer vectors, and analyzing the input layer vector features in a neural network to obtain output quantity related to a target;
and obtaining a corresponding vehicle target according to the output quantity, and outputting the vehicle target as a recognition result.
In order to accurately identify a vehicle target, it is necessary to extract vehicle-specific features from the detected image area. Feature extraction is the process of extracting and selecting information representing the features of a target from the original information of the target. At present, methods for identifying the shape of a target can be mainly classified into two types: one is based on the recognition of the shape of the edge of the object, i.e. the edge features. And secondly, identifying the shape of the area covered by the object, namely the area characteristic.
Feature extraction of the object is to take care of the following basic requirements:
(1) The extracted and selected features are insensitive to the variable parameters of the target;
(2) The characteristics are stable and easy to extract;
(3) The dimension of the feature quantity is obviously smaller than the original data of the target;
(4) The feature quantities have the smallest correlation;
(5) To improve classification accuracy, features are added that easily separate confusing categories.
Based on the above feature extraction requirements, the invention extracts the hybrid features of the vehicle (including the electric bicycle) and simultaneously includes edge features and regional features, namely 8 sub-coefficients of discrete cosine transform, edge feature parameters formed by 6 independent invariant moment parameters and 5 regional description feature parameters, and the total number of the edge feature parameters is 19.
Preferably, the discrete cosine transform sub-coefficient operation process is as follows:
the target image is cut, preprocessed and edge extracted to obtain outline data f (x m, y m), the closed edge curve formed by N points is put on complex plane to form one-dimensional complex sequence,
f(m)＝x(m)+jy(m)；1≤m≤n，
f (m) is a one-dimensional complex sequence; the discrete cosine transform of the formula is as follows:
calculating to obtain discrete cosine transform sub-coefficients:
C(k)＝|F(k)|/F(1)；
wherein C (k) is a discrete cosine transform sub-coefficient; k is the number of discrete sub-coefficients, k=1, 2 …; j is the imaginary part of the complex plane n=1, 2,3 … N-1; n is the characteristic point variable of the closed edge curve obtained by edge extraction after image segmentation, and N is the number of the characteristic points of the closed edge curve obtained by edge extraction after image segmentation.
Discrete cosine transform coefficients have translational, rotational and proportional invariance to the target and are insensitive to the starting point of the contour data. The low frequency part of the cosine transform coefficient reflects the whole outline of the image, the high frequency part only represents the detail of the outline, and the discrete cosine transform does not need complex operation and data modulo operation, and can obtain higher recognition rate by using fewer characteristic quantities.
If a digital image satisfies the segmentation continuity and there are only a limited number of zeros in the XY plane, it can be demonstrated that the moments of the orders of the digital image exist.
For a binary image, since the values of the pixels are only 0 and 1, assuming that the pixel value of the target area is 1 and the pixel value of the background area is 0, the p+q moment of the binary image is as shown in the formula:
the central moment of this region is:
wherein ,mu, the coordinates of the central point of the region pq The central moment of the area where the binarized image is located; />m 00 Is zero order geometrical moment, m of the area where the binarized image is located 01 、m 10 Is the first-order geometric moment, m, of the area where the binarized image is located pq The geometric moment of the p+q order of the area where the binary image is located, p is the row order of the central moment of the binary image, and q is the column order of the central moment of the binary image.
The target recognition characteristic parameters include: the area eccentricity, which is the ratio of the length of the major axis to the length of the minor axis of the boundary, i.e., the eccentricity of an ellipse having the same second moment as the area, is the ratio of the distance between the focal length major axes of the ellipse. Ratio of short axis to long axis of region, region area S, region perimeter L, and region compactness factor 4πS/L 2 。
Radial basis function neural networks are a novel neural network learning method that expands or preprocesses input vectors into a high-dimensional space. The method has good popularization capability, avoids complex calculation such as BP algorithm, and can realize the rapid learning of the neural network.
As shown in FIG. 3, the invention adopts RBF neural network of self-organizing selection center, and takes the edge characteristic parameters and 5 region description characteristic parameters formed by 8 discrete cosine transform coefficients, 6 independent invariant moment parameters as input vectors of the network. The discrimination result of whether the vehicle is or is not required to be recognized. Thus, the designed RBF neural network has 19-dimensional input neurons and 2 outputs.
The radial basis function neural network model is a three-layer neural network model: the first layer is an input layer, and feature vectors are input into the network. The second layer is a hidden layer, which is completely connected with the input layer (weight=1), and is equivalent to performing one-time conversion on the input mode, and converting the low-dimensional mode input data into a high-dimensional space so as to facilitate classification and identification of the output layer. Here the hidden layer node selects a gaussian radial basis function as the transfer function. The third layer is an output layer, and 2 output quantities are obtained by calculating weights between the hidden layer and the output layer, so that a vehicle target is identified.
The RBF neural network learning method of the self-organizing selection center mainly comprises two stages. Stage one: a self-organizing learning stage, namely a learning process without a teacher, and solving hidden layer basis functions; stage two: there is a teacher learning phase, i.e. solving weights between hidden layers to output layers.
The first layer is an input layer, and the feature vector is input into the network;
the second layer is a hidden layer which can be completely connected with the input layer, and the hidden layer node selects a Gaussian radial basis function as a transfer function, and the calculation formula is as follows:
in the formula ,||xp -c i I is the European norm, c i Is the center of Gaussian functionSigma is the variance of the gaussian function;
the third layer is an output layer, 2 output quantities are obtained by calculating weights between the hidden layer and the output layer, and a vehicle target is identified; the output of the network can be obtained from the structure of the radial basis function network as:
in the formula ,for the P-th input sample, p=1, 2, …, P is the total number of samples; c i Omega is the center of hidden layer node of network ij For the connection weight from the hidden layer to the output layer, i=1, 2, …, h is the number of nodes of the hidden layer, y j J=1, 2, …, n for the actual output of the j-th output node of the network for the input sample pair;
wherein ,dj Is the expected output value of the sample.
The center of the radial basis function neural network is obtained by a K-means clustering algorithm, and the specific process is as follows:
step one, randomly selecting h training samples as a clustering center c i ，i＝1,2,…h；
Step two, calculating the Euclidean distance between each training sample and the clustering center, and distributing each training sample to each clustering set psi of the input samples according to the Euclidean distance p (p=1, 2, …, P);
step three, recalculating each cluster set psi p Average value of training samples to obtain a new clustering center c i ′；
Step four, repeating the step two and the step three until a new clustering center c is obtained i ' the variation is less than a given threshold, c is obtained i ' is the final basis function center of the radial basis function neural network.
The basis function variance solving formula is:
wherein ,σi As the basis function variance, c max To select the maximum distance between centers.
The connection weight between the hidden layer and the output layer is calculated by a least square method, and the calculation formula is as follows:
as shown in fig. 4, in order to verify the effectiveness of the method for identifying the front mesh of the ADAS system based on the RBF neural network, 850 vehicle samples and 850 electric bicycle samples are respectively established, the RBF neural network is trained, 60% of positive and negative sample images are randomly selected to complete the test, and the identification accuracy of the RBF neural network can reach more than 94%. The error performance curve of the RBF neural network can be used for finding that the designed network meets the requirement of training errors.
The invention designs and develops a front target identification method of an automobile safety auxiliary system based on a radial basis function neural network, carries out edge extraction and search on a preprocessed road condition image, obtains a region of interest as a possible existing vehicle region, constructs a radial basis function neural network vehicle identifier by counting edge characteristics and region characteristic parameters of the vehicle, realizes classification of the vehicle and an electric bicycle in a detection region, greatly improves accuracy of vehicle target identification, and reduces false detection rate so as to improve reliability of system identification.
Although embodiments of the present invention have been disclosed above, it is not limited to the details and embodiments shown and described, it is well suited to various fields of use, and further modifications may be readily made by those skilled in the art without departing from the general concepts defined by the claims and the equivalents thereof, and therefore the invention is not limited to the specific details and examples shown and described herein.
Claims (7)
1. A method for identifying the front destination of an automobile safety auxiliary system based on a radial basis function neural network, which is characterized by comprising the following steps:
acquiring road condition images in front of a vehicle, and carrying out segmentation pretreatment on the road condition images;
extracting edges of the preprocessed road condition images, and searching to obtain an interested region;
extracting features of the region of interest to obtain edge features and region features corresponding to the region of interest;
constructing a radial basis neural network model by taking the edge features and the region features as input layer vectors, and analyzing the input layer vector features in a neural network to obtain output quantity related to a target;
obtaining a corresponding vehicle target according to the output quantity, and outputting the vehicle target as a recognition result;
the region of interest includes electric bicycles and motor vehicles;
the vehicle edge features and the region features corresponding to the region of interest include: edge characteristic parameters and region description characteristic parameters formed by independent invariant moment parameters of sub-coefficients of discrete cosine transform;
the discrete cosine transform sub-coefficient calculation formula is:
C(k)＝|F(k)|/F(1)；
wherein C (k) is a discrete cosine transform sub-coefficient,k is the number of discrete sub-coefficients, k=1, 2 …; f (k) =x (k) +jy (k);
j is the imaginary part of the complex plane n=1, 2,3 … N-1; n is a characteristic point variable of a closed edge curve obtained by edge extraction after image segmentationN is the number of feature points of a closed edge curve obtained by edge extraction after image segmentation, f (m) =x (m) +jy (m); m is more than or equal to 1 and less than or equal to n, and f (m) is a one-dimensional complex sequence.
2. The method for identifying the front destination of an automobile safety auxiliary system based on a radial basis function neural network according to claim 1, wherein the independent invariant moment parameter calculation formula is:
wherein ,mu, the coordinates of the central point of the region pq The central moment of the area where the binarized image is located; />m 00 Is zero order geometrical moment, m of the area where the binarized image is located 01 、m 10 Is the first-order geometric moment, m, of the area where the binarized image is located pq The geometric moment of the p+q order of the area where the binary image is located, p is the row order of the central moment of the binary image, and q is the column order of the central moment of the binary image.
3. The method for identifying a front target of an automotive safety assistance system based on a radial basis function network according to claim 2, wherein the target identification characteristic parameters include: region eccentricity, ratio of short axis to long axis of the region, region area, region perimeter, and region compactness factor.
4. A method for identifying a front target of an automotive safety assistance system based on a radial basis function network according to claim 3, wherein the radial basis function network model is a three-layer neural network model:
the first layer is an input layer, and the feature vector is input into the network;
the second layer is a hidden layer which can be completely connected with the input layer, and the hidden layer node selects a Gaussian radial basis function as a transfer function, and the calculation formula is as follows:
in the formula ,||xp -c i I is the European norm, c i Is the center of the Gaussian function, and sigma is the variance of the Gaussian function;
the third layer is an output layer, 2 output quantities are obtained by calculating weights between the hidden layer and the output layer, and a vehicle target is identified; the output of the network can be obtained from the structure of the radial basis function network as:
in the formula ,for the P-th input sample, p=1, 2, …, P is the total number of samples; c i Omega is the center of hidden layer node of network ij For the connection weight from the hidden layer to the output layer, i=1, 2, …, h is the number of nodes of the hidden layer, y j J=1, 2, …, n for the actual output of the j-th output node of the network for the input sample pair;
wherein ,dj Is the expected output value of the sample.
5. The method for identifying the front mesh of the automobile safety auxiliary system based on the radial basis function neural network according to claim 4, wherein the center of the radial basis function neural network is obtained by a K-means clustering algorithm, and the specific process is as follows:
step one, randomly selecting h training aidsTraining samples as cluster center c i ，i＝1,2,…h；
Step two, calculating the Euclidean distance between each training sample and the clustering center, and distributing each training sample to each clustering set psi of the input samples according to the Euclidean distance p (p=1, 2, …, P);
step three, recalculating each cluster set psi p Average value of training samples to obtain a new clustering center c i ′；
Step four, repeating the step two and the step three until a new clustering center c is obtained i ' the variation is less than a given threshold, c is obtained i ' is the final basis function center of the radial basis function neural network.
6. The method for identifying the front mesh of an automotive safety auxiliary system based on a radial basis function neural network according to claim 5, wherein the basis function variance solving formula is:
wherein ,σi As the basis function variance, c max To select the maximum distance between centers.
7. The method for identifying the front mesh of the automobile safety auxiliary system based on the radial basis function neural network according to claim 6, wherein the connection weight between the hidden layer and the output layer is calculated by a least square method, and the calculation formula is as follows:
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