CN110069993A - A kind of target vehicle detection method based on deep learning - Google Patents

A kind of target vehicle detection method based on deep learning Download PDF

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CN110069993A
CN110069993A CN201910206458.5A CN201910206458A CN110069993A CN 110069993 A CN110069993 A CN 110069993A CN 201910206458 A CN201910206458 A CN 201910206458A CN 110069993 A CN110069993 A CN 110069993A
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target vehicle
convolutional neural
neural networks
vehicle detection
deep learning
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CN110069993B (en
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瞿三清
许仲聪
卢凡
陈广
董金虎
陈凯
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Tongji University
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Abstract

The target vehicle detection method based on deep learning that the present invention relates to a kind of, the following steps are included: 1) acquire the tail feature point cloud data of target vehicle by the way that two single line laser radars of parking robot tail portion are arranged in, and pre-processed to obtain binary picture；2) binary picture is labeled, obtains the wherein position where target vehicle tail portion, training dataset is generated with this；3) building is suitable for the depth convolutional neural networks and its loss function of target vehicle detection；4) it is inputted in depth convolutional neural networks after training dataset being carried out augmentation, and update is trained to the parameter in convolutional neural networks according to the difference of output valve and training true value, optimal network parameter is obtained, and is detected according to trained depth convolutional neural networks.Compared with prior art, the present invention has the advantages that robustness is high, it is low etc. not depend on manual feature, testing cost.

Description

A kind of target vehicle detection method based on deep learning
Technical field
The present invention relates to intelligent parking technical fields, more particularly, to a kind of target vehicle detection side based on deep learning Method.
Background technique
In intelligent driving field, the detection for target vehicle is key for ensureing automatic driving vehicle safety traffic One of business.Equally in intelligent parking technical field, the position of target vehicle is detected, is to realize parking robot to target vehicle essence The committed step of quasi- contraposition.Since laser radar is affected by environment small, target vehicle accurately point cloud data, laser can be acquired Radar has become the sensor of vehicle detection and positioning mostly important in intelligent parking field.
Currently, in intelligent parking technical field, it is also main using based on target vehicle to the detection method of target vehicle The traditional detection algorithm of manual feature.Traditional detection algorithm, although calculation amount is small, fast speed, under many scenes The feature of target vehicle can not match well with manual feature, cause that conventional target vehicle detecting algorithm recall rate is low, Shandong Stick is poor.
Summary of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind to be based on deep learning Target vehicle detection method.
The purpose of the present invention can be achieved through the following technical solutions:
A kind of target vehicle detection method based on deep learning, to realize intelligent parking, comprising the following steps:
1) the tail feature point of target vehicle is acquired by the way that two single line laser radars of parking robot tail portion are arranged in Cloud data, and pre-processed to obtain binary picture；
2) binary picture is labeled, obtains the wherein position where target vehicle tail portion, trained number is generated with this According to collection；
3) building is suitable for the depth convolutional neural networks and its loss function of target vehicle detection；
4) it is inputted in depth convolutional neural networks after training dataset being carried out augmentation, and according to output valve and training true value Difference update is trained to the parameter in convolutional neural networks, obtain optimal network parameter, and according to trained depth Degree convolutional neural networks are detected.
The step 1) specifically includes the following steps:
11) collected point cloud data is converted to using single line laser radar as the polar coordinate system of coordinate origin global unified Cartesian coordinate system；
12) to the meshing point cloud data after coordinate conversion, binary picture is converted to.
In the step 11), transformed representation are as follows:
(xj0,yj0)=(xj1,yj1)R+t
Wherein, (rj,φj) be original point cloud data midpoint j position coordinates, (xj1,yj1) it is that point j is converted to laser thunder Up to the position coordinates in the cartesian coordinate system for coordinate origin, (xj0,yj0) it is the global seat unified in cartesian coordinate system Mark, R are conversion spin matrix, and t is translation vector.
In the step 2), marked content includes that the Pixel-level mark of image and the bounding box of target vehicle mark.
The depth convolutional neural networks are Faster R-CNN convolutional neural networks, with the binary system being sized Image is as input, to export with position and the confidence level of target vehicle is corresponded on input binary picture.
The expression formula of the loss function of depth convolutional neural networks are as follows:
Loss=Lcls(p, u)+λ [u=1] Lloc(tu,v)
Lcls(p, u)=- log (p)
X=(tu-v)
Wherein, Lcls(p, u) is target classification Detectability loss subfunction, Lloc(tu, v) and it is range loss subfunction, p is pair In the predictive factor of target category, u is the practical factor of corresponding classification, and λ is the weighting weight of loss function, is indicated as u=1 It is target vehicle in area-of-interest, indicates that area-of-interest is background, t as u=0uFor the location factor of prediction, v is instruction Practice true location factor in sample, x is the deviation of predicted value and true value.
Augmentation is carried out to training dataset specifically:
Image is carried out Random Level overturning, cuts and uniformly zooms to fixed size, and labeled data also carries out Corresponding overturning cuts and scales.
Training depth convolutional neural networks specifically:
According to loss function, declines back-propagation method using gradient, change to the parameter of depth convolutional neural networks In generation, updates, and the network parameter obtained after iteration to maximum is set number completes training as optimal network parameter.
Compared with prior art, the invention has the following advantages that
One, robustness is high: since laser radar can acquire accurate target vehicle under the operating condition of various complexity Point cloud data is aided with the target vehicle detection algorithm of robustness with higher, and therefore, vehicle checking method of the invention has Very high robustness can also guarantee the relative precision of testing result under complex working condition.
Two, do not depend on manual feature: the present invention passes through depth mind compared to traditional target vehicle detection algorithm, this method Target signature is learnt through network, does not depend on manual feature, the recall rate of testing result is high.
Three, testing cost is low: the present invention uses two single line laser radars as sensor, compared to multi-thread laser thunder It reaches, testing cost is low.
Detailed description of the invention
Fig. 1 is the flow chart of detection method of the invention.
Fig. 2 is intelligent parking robot architecture's schematic diagram in the embodiment of the present invention.
Fig. 3 is target vehicle depth convolutional network structural schematic diagram in the embodiment of the present invention.
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
The present invention provides a kind of based on deep learning using the method for the target vehicle detection of single line laser radar, leads to The sensor that single line laser radar is crossed as detection obtains the point cloud data of target vehicle, and depth volume is input to after being pre-processed Product neural network, finally obtains position and the confidence level of target vehicle.As shown in Figure 1, this method comprises the following steps:
(1) using the tail feature point cloud data of 2 single line laser radar acquisition target vehicles, and to collected cloud Data are pre-processed；
(2) acquisition data are passed through with the tail portion position for manually marking wherein target vehicle, constructs training data used Collection
(3) building is suitable for the depth convolutional neural networks and loss function of target vehicle detection
(4) data used in the training of step (2) are subjected to augmentation, are input to depth convolutional Neural constructed by step (3) In network.Update is trained to the parameter in convolutional neural networks according to the difference of output valve and training true value, is finally obtained Ideal network parameter.
It in the present embodiment, include to the coordinate conversion of point cloud data and figure to the pretreatment of point cloud data in step (1) As two following steps such as conversions:
2 single line laser radars in (1-1) the present embodiment are located at the rear two sides of intelligent parking robot, intelligent parking Robot architecture is as shown in Fig. 2, two single line laser radars are by the point cloud data around the acquisition of definitely frame per second, and collection result is to swash Optical radar is coordinate origin, and is stored with polar form.The point cloud data of acquisition is converted to the overall situation system of parking robot In one cartesian coordinate system.
Transformed representation is as follows:
(xj0,yj0)=(xj1,yj1)R+t
In above formula, (rj,φj) indicate acquisition original point cloud data in certain point, (xj1,yj1) it is the point cloud number acquired Certain point in is converted to using laser radar to indicate in the cartesian coordinate system of coordinate origin.(xj0,yj0) it is then correspondence Point cloud data point converts the expression into the unified cartesian coordinate system of the overall situation of parking robot.R is conversion spin matrix, and t is Translation vector.
(1-2), to point cloud data imageization processing.In the present embodiment, what laser radar acquired is set as apart from the upper limit Then 10m, the meshing point cloud data after coordinate is converted adjust the binary picture having a size of 250 × 250, if corresponding There is data point in grid, is then set to 1 and is otherwise set to 0.
In the present embodiment, step (2) is to construct the required data set of deep learning training.In the laser radar to acquisition It after point cloud data is handled, needs manually to mark training data, to form the required data set of training.The mode of mark The including but not limited to bounding box mark of the Pixel-level mark of image, target vehicle.Target vehicle need to be included at least when mark Position, but the posture information etc. for increasing target vehicle can be expanded.
In the present embodiment, step (3) is depth convolutional neural networks and the loss that building is suitable for target vehicle detection Function.The training dataset prepared in the building of depth convolutional neural networks and step (2) is directly related, in the present embodiment, Step (2) using target vehicle bounding box mark, therefore in the present embodiment the structure of depth convolutional neural networks with Faster R-CNN is similar, and main structure is built with reference to Faster R-CNN, and convolutional neural networks structure is as shown in Figure 3.
In the present embodiment, in step (3), the loss function of depth convolutional neural networks is constituted with two parts weighting:
Loss=Lcls(p, u)+λ [u=1] Lloc(tu,v)
(3-1) constructs target classification loss function Lcls(p, u), wherein p is the predictive factor for target category, and u table Show the practical factor of corresponding classification.The building of log loss function is generallyd use, wherein p indicates that the prediction probability of a certain classification, p are got over Close to 1, confidence level is higher, loses smaller.
Lcls(p, u)=- log (p)
(3-2) constructs target detection range Lloc(tu,v).Wherein λ indicates the weighting weight of loss function, can usually take λ=1.[u=1] indicates in area-of-interest to be that target vehicle takes 1, takes 0 when area-of-interest is background, i.e., if current When area-of-interest is environment unrelated things, its range loss is not considered.T in formulauIndicate to represent predicted position because Son, and v indicates true location factor in training sample.Usually the range loss subfunction is public with smooth manhatton distance FormulaBuilding, expression formula are as follows: wherein x=(tu- v), indicate the deviation of predicted value and true value.
In the present embodiment, step (4) mainly includes turning over image into row stochastic level to the augmentation of training data Turn, cut and uniformly zoom to fixed size, labeled data is also overturn accordingly, cut and scaled, on this basis Obtained image is normalized by channel, the fixed dimension used in the present embodiment is 250 × 250.
In the present embodiment, when step (4) is to the initialization for training network model, first in ImageNet or other figures Pre-training is carried out as extracting network to object features using SoftMax loss function on categorized data set, obtained parameter value is made For the initial parameter of network.
In the present embodiment, when in step (4) to the training of network, comprehensive loss is calculated using the loss function of weighting Then value carries out backpropagation and calculates gradient, and updates network parameter using optimizers such as Adam, the certain number of iteration obtains most Whole result.And set final parametric results to the network model parameter of target vehicle detection device, for detecting target carriage Use.
The present invention provides a kind of based on deep learning using the method for the target vehicle detection of single line laser radar, leads to The sensor that single line laser radar is crossed as detection obtains the point cloud data of target vehicle, and depth volume is input to after being pre-processed Product neural network, finally obtains position and the confidence level of target vehicle.The detection performance is outstanding, while having higher robustness, Cost of implementation is low, is easy to be deployed to the detection that target vehicle is used in existing intelligent parking robot.
Person skilled in the art obviously easily can make various modifications to these embodiments, and saying herein Bright General Principle is applied in other embodiments without having to go through creative labor.Therefore, the present invention is not limited to here Embodiment, those skilled in the art's announcement according to the present invention, improvement and modification made without departing from the scope of the present invention are all answered This is within protection scope of the present invention.

Claims (8)

1. a kind of target vehicle detection method based on deep learning, to realize intelligent parking, which is characterized in that including following Step:
1) the tail feature point cloud number of target vehicle is acquired by the way that two single line laser radars of parking robot tail portion are arranged in According to, and pre-processed to obtain binary picture；
2) binary picture is labeled, obtains the wherein position where target vehicle tail portion, training dataset is generated with this；
3) building is suitable for the depth convolutional neural networks and its loss function of target vehicle detection；
4) it is inputted in depth convolutional neural networks after training dataset being carried out augmentation, and according to the difference of output valve and training true value The different parameter in convolutional neural networks is trained update, obtains optimal network parameter, and roll up according to trained depth Product neural network is detected.
2. a kind of target vehicle detection method based on deep learning according to claim 1, which is characterized in that described Step 1) specifically includes the following steps:
11) collected point cloud data is converted into global unified flute by the polar coordinate system of coordinate origin of single line laser radar Karr coordinate system；
12) to the meshing point cloud data after coordinate conversion, binary picture is converted to.
3. a kind of target vehicle detection method based on deep learning according to claim 1, which is characterized in that described In step 11), transformed representation are as follows:
(xj0,yj0)=(xj1,yj1)R+t
Wherein, (rj,φj) be original point cloud data midpoint j position coordinates, (xj1,yj1) it is that point j is converted to and is with laser radar Position coordinates in the cartesian coordinate system of coordinate origin, (xj0,yj0) it is the global coordinate unified in cartesian coordinate system, R is Spin matrix is converted, t is translation vector.
4. a kind of target vehicle detection method based on deep learning according to claim 1, which is characterized in that described In step 2), marked content includes that the Pixel-level mark of image and the bounding box of target vehicle mark.
5. a kind of target vehicle detection method based on deep learning according to claim 1, which is characterized in that described Depth convolutional neural networks are Faster R-CNN convolutional neural networks, using the binary picture that is sized as inputting, To be exported with position and the confidence level of target vehicle is corresponded on input binary picture.
6. a kind of target vehicle detection method based on deep learning according to claim 1, which is characterized in that described In step 3), the expression formula of the loss function of depth convolutional neural networks are as follows:
Loss=Lcls(p, u)+λ [u=1] Lloc(tu,v)
Lcls(p, u)=- log (p)
X=(tu-v)
Wherein, Lcls(p, u) is target classification Detectability loss subfunction, Lloc(tu, v) and it is range loss subfunction, p is for mesh The predictive factor of classification is marked, u is the practical factor of corresponding classification, and λ is the weighting weight of loss function, indicates feeling as u=1 Interest region is target vehicle, indicates that area-of-interest is background, t as u=0uFor the location factor of prediction, v is training sample True location factor in this, x are the deviation of predicted value and true value.
7. a kind of target vehicle detection method based on deep learning according to claim 1, which is characterized in that described In step 4), augmentation is carried out to training dataset specifically:
Image is carried out Random Level overturning, cuts and uniformly zooms to fixed size, and labeled data also carries out accordingly Overturning, cutting and scaling.
8. a kind of target vehicle detection method based on deep learning according to claim 1, which is characterized in that described In step 4), training depth convolutional neural networks specifically:
According to loss function, declines back-propagation method using gradient, the parameter of depth convolutional neural networks is iterated more Newly, the network parameter obtained after iteration to maximum being set number completes training as optimal network parameter.
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