CN108009525A - A kind of specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks - Google Patents

A kind of specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks Download PDF

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CN108009525A
CN108009525A CN201711422364.9A CN201711422364A CN108009525A CN 108009525 A CN108009525 A CN 108009525A CN 201711422364 A CN201711422364 A CN 201711422364A CN 108009525 A CN108009525 A CN 108009525A
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张弘
罗昭慧
张泽宇
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Abstract

The present invention relates to a kind of unmanned plane based on convolutional neural networks, specific objective recognition methods is as follows over the ground：(1) data set of specific objective over the ground that unmanned plane collects is labeled according to classification, and proportionally divides training set, verification collection, test set；(2) training convolutional neural networks model parameter, training parameter are set, starts to train, according to training, obtains the optimal solution of convolutional neural networks model.(3) change convolutional neural networks model depth, restart (2) training, obtain optimal convolutional neural networks model；(4) test set is tested, obtains recognition accuracy；(5) the convolutional neural networks model parameter that accuracy rate is met the requirements is applied to unmanned plane over the ground among specific objective actual scene, the image object that unmanned plane collects is identified.

Description

A kind of specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks
Technical field
The present invention relates to it is a kind of based on the unmanned plane of convolutional neural networks to specific objective recognition methods, belong to aviation boat with Computer vision information processing crossing domain.
Background technology
Unmanned plane (UmannedAir Vehicle, UAV), is filled using radio robot and the programme-control provided for oneself The not manned aircraft of manipulation is put, or fully or is intermittently independently operated by car-mounted computer.Unmanned plane is led according to application Domain, can be divided into it is military with it is civilian.Military aspect, unmanned plane are divided into reconnaissance plane and target drone.Civilian aspect, unmanned plane+sector application, It is that unmanned plane has really just needed；At present take photo by plane, agricultural, plant protection, miniature self-timer, express transportation, disaster relief, observation it is wild dynamic Thing, monitoring infectious disease, mapping, news report, electric inspection process, the disaster relief, movies-making, etc. field application, greatly expand The purposes of unmanned plane in itself.
Traditional target identification, mainly first to expressing this figure with mathematical model after a certain amount of manual features of image zooming-out Picture, is then identified image by grader.For example, object identification uses scale invariant feature (Scale- Invariant feature transform, SIFT), recognition of face uses Local textural feature (Local Binary Patterns, LBP), pedestrian detection using histograms of oriented gradients feature (Histogram of Oriented Gradient, HOG), traditional grader mainly has support vector machines (Support Vector Machine, SVM), K arest neighbors (k- NearestNeighbor, KNN) etc..But the identification of the machine learning method of this kind of shallow-layer is low, it generally can not meet unmanned plane The engineering demand of target identification over the ground.
With the development of artificial intelligence, the continuous breakthrough of deep learning, in speech recognition, natural language processing, computer The fields such as vision, video analysis, multimedia all achieve immense success.Convolutional neural networks target identification based on deep learning Flow is mainly：Image is input in neutral net, utilizes the propagated forward and reverse propagated error scheduling algorithm of deep learning To minimize loss function, after updating weights, obtain a preferably identification model, then using this model to new image come It is identified.Convolutional neural networks model can from view data automatic learning characteristic, and can be fast from new training data Speed training is so as to learn to new character representation.General pattern recognition system is all important comprising feature extraction and grader two Part, their optimization is to be separated from each other in traditional method, and under the frame of neutral net, feature extraction and grader It is that joint is feedback optimized, the performance of the two integration and cooperation can be played as far as possible.
But it is very strong to the dependence of training dataset based on the target identification method of convolutional neural networks, and unmanned plane is adopted The image data amount size of the target over the ground collected is not known, and is differed and is surely met the training demand of convolutional neural networks model, Or network depth not enough causes a degree of waste to the information of data set.Therefore the present invention combines unmanned plane collection image The characteristics of with convolutional neural networks model the advantages of, according to data set adjust network model depth, obtain optimal network model knot Structure, so as to propose a kind of convolutional neural networks target identification method that can adapt to unmanned plane collection image data set size.
The content of the invention
The technology of the present invention solves the problems, such as：Overcoming unmanned plane, conventional method recognition accuracy is inadequate in terms of target identification over the ground Problem and the implacable problem of deep learning method training dataset, there is provided a kind of unmanned plane based on convolutional neural networks Specific objective recognition methods over the ground, the target data set size over the ground collected according to unmanned plane, to adjust the depth of network model Degree, realizes the recognition accuracy of higher, so as to provide a kind of unmanned plane effective way that specific objective identifies over the ground.It can also answer In the Pattern Recognition and Intelligent System design complicated for other.
The technology of the present invention solution：A kind of specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks, Include the following steps：
(1) using unmanned plane collection specific objective image data set over the ground, it is labeled according to classification, and according to having marked Data set be divided into training set, verification collection, test set, be finally processed into the data class that convolutional neural networks model can identify Type；
(2) structure identification convolutional neural networks model, sets maximum iteration, learning rate, test frequency, and selection is reverse Transmission method, starts to train according to above-mentioned be provided and selected, and then according to training loss function situation of change, obtains this convolution god Recognition accuracy through network model；
(3) in the structure of convolutional neural networks model, increase or the reduction convolution number of plies, restart step (2) Training, when training convolutional neural networks model identification rate of accuracy reached to highest when, illustrate this train convolutional neural networks Model adapts to current data set size, and the training retained at this time obtains the structure and parameter of convolutional neural networks model；
(4) the convolutional neural networks model for utilizing (3) to obtain, tests test set, obtains recognition accuracy, to knowing Other accuracy rate is judged, if recognition accuracy disclosure satisfy that actual requirement of engineering, the convolutional neural networks model It is applicable in the actual unmanned plane task that specific objective identifies over the ground, performs step (5)；If not satisfied, then explanation is instructed Actual requirement of engineering cannot be met, it is necessary to expand training set by practicing collection, restart step (1), (2), (3), until meeting actual Untill engineering；
(5) accuracy rate is met to the parameter of the convolutional neural networks model of Practical Project requirement, and to be applied to unmanned plane special over the ground Among the actual scene to set the goal, the image object that unmanned plane collects is identified.
In the step (2), the method for obtaining the convolutional neural networks model of parametric optimal solution is：When the loss of training set Function Loss falls be no more than 0.001 when, and verify collection loss function Loss tend to rise critical point when, that is, obtain The convolutional neural networks model of parametric optimal solution.
In the step (2), maximum iteration, learning rate, test frequency are set, select back-propagation method specifically such as Under：
Maximum iteration：200000 times；
Initial learning rate：0.001；
Test frequency：1000 iteration/1 time；
Back-propagation method：Stochastic gradient descent algorithm.
Convolutional neural networks model is 5 convolutional layers in the step (2), plus 3 full articulamentums.
The number of plies is increased or decreased in the step (3) no more than two layers.
The present invention compared with prior art the advantages of be：
(1) using convolutional neural networks model, to unmanned plane, specific objective is identified the present invention over the ground, and unmanned plane is over the ground Specific objective species is more, such as ships, pedestrian, the change of vehicle these target signatures are greatly, compared to conventional method, engineer's Feature is difficult to give expression to target information completely, carries out feedback learning using convolutional neural networks, can learn to more having Shandong The feature representation of rod, so as to ensure recognition accuracy requirement；
(2) convolutional neural networks model training method proposed by the present invention, is damaged by the change of training loss function with test Function situation of change is lost, to obtain globally optimal solution, in contrast to the general target identification method based on convolutional neural networks, is led to Prior information is crossed to set fixed number of iterations, the present invention can improve training effectiveness, while can also improve convolutional neural networks The recognition accuracy of model；
(3) different depth convolutional neural networks model construction method proposed by the present invention, for different data sets, changes Network model depth is trained test, obtains the optimal convolutional neural networks model of depth.General convolutional neural networks model Target identification method, usual network architecture is fixed, and available data collection cannot meet its training demand well, or Cause data set information to waste, the present invention can adapt to different data sets, so as to fulfill convolutional neural networks model structure with The optimization of parameter.
Brief description of the drawings
Fig. 1 is the flow diagram of the unmanned plane based on convolutional neural networks of the invention specific objective recognition methods over the ground；
Fig. 2 is the convolutional neural networks model structure of the present invention；
Fig. 3 is convolutional neural networks model loss function change curve in training process in the present invention；
Fig. 4 is convolutional neural networks model accuracy rate change curve in training process in the present invention.
Fig. 5 is the test result figure of the present invention.
Figure label and symbol description are as follows：
Jcv(θ) --- cross validation collection loss function change curve；
Jtrain(θ) --- training set loss function change curve；
Iteration --- training iterations；
Test accuracy --- training is test accuracy rate change curve.
Embodiment
The present invention is described in detail with reference to the accompanying drawings and embodiments.
First, training algorithm
Have the identification mission of supervision for convolutional neural networks, due to know in advance all image patterns classification, it is necessary to According to distribution of its identical image sample in space so that differ the sample distribution of classification on different area of space.Through Long-time training image data set is crossed, constantly updates the middle parameter of convolutional neural networks, obtains dividing sample space classification Boundary position classify to image.Convolutional neural networks are substantially a kind of mappings for being input to output, according to spy Fixed principle learns Function Mapping, which is mapped to an input picture feature vector of one k dimension.As long as to convolution Network training, obtains the connection weight between network, and by activation primitive, network will learn to reflecting between inputoutput pair Penetrate ability.
Training algorithm is broadly divided into propagated forward stage and back-propagation phase：
First stage, forward propagation stage：Sample set (X, Y) is input to network, wherein X represents sample data, and Y is represented Sample label.Calculated by the level of network, i.e. input and every layer of weight matrix phase dot product, successively obtains corresponding after computing Output valve hθ(x).During the propagated forward stage, the weights of network random initializtion network connection.
Second stage, back-propagation phase：Calculate reality output hθ(x) error between corresponding preferable output Y, i.e., Cost function value.Weight matrix is adjusted by the method backpropagation of minimization error.
2nd, cost function (LOSS)：
For network output signal hθ(x) it is the vector that a dimension is k with target desired signal y.Neutral net exports With the error of actual value, i.e. loss function, calculated using Euclidean distance, the cost function of neutral net is represented by：
Calculated in layer using Positive Propagation Algorithm is positive since first layer, obtain every layer and obtained by activation primitive As a result, to the last one layer.But in order to make the loss function value of whole network minimum, it is necessary to which constantly iteration updates nerve Parameter and deviation before member.Here back-propagation algorithm is used, i.e., by calculating the error of last layer first, then again successively The error of each layer is reversely obtained, weights and biasing are updated using obtained residual error, so as to calculate network model Least disadvantage function.
3rd, back-propagation algorithm (Back propagation, BP)：
Most of neural network model, can use based on gradient descent method to solve the parameter of network, and in network , it is necessary to use back-propagation algorithm when training sample is updated parameter.The first step, first initializes the weight parameter of network For one group of random value；Second step, reuses training data and is iterated training.Calculate the output of neural network model and it is expected defeated Error between going out, i.e. loss function value, input is propagated to forward by error in layer from last layer, and is calculated according to gradient Method updates the weights of each layer network, until meeting condition or stopping more than maximum iteration.
When back-propagation algorithm is applied to convolutional neural networks, BP algorithm step is as follows：Assuming that activation primitive is Sigmoid functions, neutral net one share m layers, and kth layer has skA neuron, for i-th of neuron of kth layer, WijRepresent The weight coefficient being attached thereto, then there are s for last layerk-1A weight coefficient Wi1,Wi2,···,Wij,···,Therewith It is connected, biRepresent biasing.
The step of execution of BP algorithm, is as follows：
(1) to weight coefficient WijIt is random to put initial value, a sample (x, y) is inputted, wherein x represents sample, and y represents it is expected defeated Go out；
(2) every layer of output, the output for k layers of i-th of neuron are calculatedHave：
(3) error of each layer study is sought
There is k=m for output layer,
To other each layers, have
(4) according to errorModified weight coefficient WijWith biasing bi
Wherein,Represent the of -1 layer of kthjA output, t represent iterations, and η represents learning rate, and α represents random Gradient momentum.
(5) after the weights of each layer of convolutional neural networks have been obtained, according to condition judge whether to meet.If demand expires Foot, then algorithm terminates；If do not met, return to (2) and perform.
4th, solution
The method of the present invention, as shown in Figure 1, comprising the following steps that：
(1) target data set over the ground for collecting unmanned plane is labeled according to classification, the view data that will have been marked Collect according to a certain percentage, i.e., 3:1:1 is divided into training set, verification collection, test set, is then convolutional Neural by image data set processing The data type that network model can identify.
(2) build convolutional neural networks model, as shown in Fig. 2, above five layers be convolutional layer, behind two layers be to connect entirely Layer, last layer are classification output layers, wherein there is 4 times of down-sampled, second convolutional layers between input layer and first convolutional layer Have between the 3rd convolutional layer 2 times it is down-sampled, have between the 5th convolutional layer and first full articulamentum 2 times it is down-sampled.If Training parameter, training method are put, such as：Maximum iteration, learning rate, test frequency, back-propagation method etc., start to train This model.According to training loss function situation of change, optimal solution at this time is obtained.When the loss function (Loss) of training set declines Slowly (amplitude of variation be less than 0.001), when verifying the loss function (Loss) of collection tends to the critical point risen, network parameter reaches To global optimum, the convolutional neural networks model parameter that training obtains at this time is preserved.
(3) in the structure of convolutional neural networks model, appropriate increase or the convolution for reducing convolutional neural networks model The number of plies (increases or decreases the number of plies no more than two layers), restarts to train, when the rate of accuracy reached of convolutional neural networks Model Identification During to highest, illustrate that this convolutional neural networks model compares and adapt to current data set size, retain convolutional Neural net at this time Network model structure and parameter.
(4) the convolutional neural networks model for utilizing (3) to obtain, tests test set, obtains recognition accuracy, work as knowledge Other accuracy rate meets actual requirement of engineering, this convolutional neural networks model may be used among actual identification mission, if not satisfied, Then illustrate that training dataset cannot meet actual requirement of engineering, then need dilated data set, restart (1) (2) (3) step.
(5) the convolutional neural networks model parameter that accuracy rate is met the requirements is applied to unmanned plane specific objective reality over the ground Among scene, the image object that unmanned plane collects is identified.
The unmanned plane proposed by the invention based on deep learning specific mesh over the ground is verified by a specific embodiment The performance of recognition methods is marked, used is the images to be recognized data set that unmanned plane vision system is gathered, in this, as testing Demonstrate,prove object, the deep learning frame based on caffe1.
As shown in Figure 1, the present invention's comprises the following steps that：
(1) image is pre-processed：
The image data set that unmanned plane collects is categorized as 3 class targets (ship, pedestrian, vehicle), 6000 altogether, by data Image is concentrated to be labeled according to classification, by the image data set marked according to 3:1:1 be divided into training set, cross validation collection, Test set.Then the data type that image data set processing can be identified for network model, changes into lmdb forms.
(2) convolutional neural networks network model is constructed
According to convolutional neural networks model structure, such as scheme, shown in 2, write the prototxt files of network structure, before 5 Layer network is convolutional layer, behind 3 layer networks be full articulamentum.Wherein, Stride of 4 represent that convolution step-length is 4, Max Pooling is represented using the down-sampled method of maximum.
(3) training network model：
Training parameter is set first：Maximum iteration is set 200,000 times, initial learning rate is 0.001, using boarding steps Degree declines back-propagation algorithm, starts training network model.
Training is observed, obtains the optimal solution of convolutional neural networks model.As shown in figure 3, convolution god in training process Through network model loss function change curve, when the loss function (Loss) of training set declines loss letter that is slow, and verifying collection When number (Loss) tends to the critical point risen, convolutional neural networks model parameter reaches globally optimal solution.It is as shown in figure 4, final The test accuracy rate of training pattern illustrates that the target identification accuracy rate of this convolutional neural networks model is higher 0.99 or so.
(4) network model is changed：
Increase by 1 Ge Juan basic units in the structure of convolutional neural networks model as shown in Figure 1, restart to train, find Final goal recognition accuracy accuracy slightly declines 0.5.1 is reduced in the structure of convolutional neural networks model as shown in Figure 1 Ge Juan basic units, restart to train, it is found that final goal recognition accuracy slightly declines 0.3.Illustrate the convolution god of 5 convolutional layers The demand of this data set is more conform with through network model, it is not necessary to make an amendment again.
(5) test network model：
The network parameter that step (3) obtains is imported among test network, as shown in figure 5, for unmanned plane over the ground three Class target：Ship, vehicle, the recognition result of pedestrian, are identified test image, and it is all correct to obtain recognition result.
Above example is provided just for the sake of the description purpose of the present invention, and is not intended to limit the scope of the present invention.This The scope of invention is defined by the following claims.The various equivalent substitutions that do not depart from spirit and principles of the present invention and make and repair Change, should all cover within the scope of the present invention.

Claims (5)

  1. A kind of 1. specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks, it is characterised in that：Including following step Suddenly：
    (1) specific objective image data set over the ground is gathered using unmanned plane, is labeled according to classification, and according to the number marked It is divided into training set, verification collection, test set according to collection, is finally processed into the data type that convolutional neural networks model can identify；
    (2) structure identification convolutional neural networks model, sets maximum iteration, learning rate, test frequency, selects backpropagation Method, starts to train according to above-mentioned be provided and selected, and then according to training loss function situation of change, obtains this convolutional Neural net The recognition accuracy of network model；
    (3) in the structure of convolutional neural networks model, increase or reduce the convolution number of plies, restart the training of step (2), When training convolutional neural networks model identification rate of accuracy reached to highest when, illustrate this training convolutional neural networks model fit Current data set size is answered, the training retained at this time obtains the structure and parameter of convolutional neural networks model；
    (4) the convolutional neural networks model for utilizing (3) to obtain, tests test set, obtains recognition accuracy, accurate to identification True rate is judged that, if recognition accuracy disclosure satisfy that actual requirement of engineering, the convolutional neural networks model can It is applied in the actual unmanned plane task that specific objective identifies over the ground, performs step (5)；If not satisfied, then illustrate training set It cannot meet actual requirement of engineering, it is necessary to expand training set, restart step (1), (2), (3), until meeting Practical Project Untill；
    (5) accuracy rate being met to, the parameter of the convolutional neural networks model of Practical Project requirement is applied to unmanned plane specific mesh over the ground Among target actual scene, the image object that unmanned plane collects is identified.
  2. 2. the specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks according to claim 1, its feature It is：In step (2), the method for obtaining the convolutional neural networks model of parametric optimal solution is：When the loss function of training set Loss falls be no more than 0.001 when, and verify collection loss function Loss tend to rise critical point when, that is, obtain parameter The convolutional neural networks model of optimal solution.
  3. 3. the specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks according to claim 1, its feature It is：In step (2), maximum iteration, learning rate, test frequency are set, select back-propagation method specific as follows：
    Maximum iteration：200000 times；
    Initial learning rate：0.001；
    Test frequency：1000 iteration/1 time；
    Back-propagation method：Stochastic gradient descent algorithm.
  4. 4. the specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks according to claim 1, its feature It is：Identification convolutional neural networks model is 5 convolutional layers in step (2), plus 3 full articulamentums.
  5. 5. the specific objective recognition methods over the ground of the unmanned plane based on convolutional neural networks according to claim 1, its feature It is：The number of plies is increased or decreased in step (3) no more than two layers.
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