CN109948684A - Quality detecting method, device and its relevant device of point cloud data mark quality - Google Patents
Quality detecting method, device and its relevant device of point cloud data mark quality Download PDFInfo
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Abstract
The invention discloses quality detecting method, device and its relevant devices of a kind of point cloud data mark quality.Wherein, which includes: acquisition point cloud data；Point cloud data is detected respectively according to N number of disaggregated model that preparatory training obtains, obtains detection classification results of the point cloud data under N number of disaggregated model；Assessment is compared with the labeled data of point cloud data respectively in detection classification results of the point cloud data under N number of disaggregated model, obtains N number of assessment score of point cloud data；According to N number of assessment score, quality inspection is carried out to the mark quality of point cloud data.N number of assessment score that this method passes through calculating point cloud data, then quality inspection is carried out according to mark quality of the assessment score to point cloud data, so as to which reasonably the mark quality to the point cloud data quality inspection can be carried out by the assessment score of the point cloud data, " accidentally doubtful mark, spill tag " data area can be effectively reduced, and realize automation finds out problematic data.
Description
Technical field
The present invention relates to the quality detecting method of electronic data processing field more particularly to a kind of mark quality of point cloud data,
Device, computer equipment and computer readable storage medium.
Background technique
Constantly expand with the comprehensive development of social economy and to spatial information demand, is perceived in current automatic Pilot
In technology, usually perceived with 3D (Three Dimensions, three-dimensional) laser radar point cloud data, in order to make laser thunder
Danone enough obtains more structural informations, and is able to carry out high-precision data scanning and processing, uses laser radar point cloud
Data are calculated, and point cloud data is labeled be algorithm training in key point.And the mark quality of data is good
Bad is again the quality for directly affecting model.
In the related technology, it when the mark quality to point cloud data carries out quality inspection, is generally seen by artificial Quality Inspector's naked eyes
It examines and judges whether there is marking error.However, this by way of artificial quality inspection, may because of data volume big, artificial carelessness
Etc. reasons there is human error, that is, there is the case where " accidentally doubtful mark, spill tag ", and higher cost.Therefore, how to realize to point
The mark quality of cloud data carries out quality inspection, is asked with effectively reducing " doubtful accidentally mark, spill tag " data area and having found out for automation
The data of topic, have become urgent problem to be solved.
Summary of the invention
The purpose of the present invention is intended to solve above-mentioned one of technical problem at least to a certain extent.
For this purpose, the first purpose of this invention is to propose a kind of quality detecting method of point cloud data mark quality, this method
Reasonably the mark quality to the point cloud data quality inspection can be carried out by the assessment score of the point cloud data, it can be effective
" accidentally doubtful mark, spill tag " data area is reduced, and realize automation finds out problematic data, reduces cost of labor.
Second object of the present invention is to propose a kind of quality inspection device of point cloud data mark quality.
Third object of the present invention is to propose a kind of computer equipment.
Fourth object of the present invention is to propose a kind of computer readable storage medium.
In order to achieve the above objectives, the quality detecting method for the point cloud data mark quality that first aspect present invention embodiment proposes,
It include: acquisition point cloud data, wherein the point cloud data is the point cloud data by mark；It is obtained according to preparatory training N number of
Disaggregated model respectively detects the point cloud data, obtains detection of the point cloud data under N number of disaggregated model
Classification results, wherein N is positive integer；By detection classification results of the point cloud data under N number of disaggregated model respectively with
Assessment is compared in the labeled data of the point cloud data, obtains N number of assessment score of the point cloud data；According to described N number of
Score is assessed, quality inspection is carried out to the mark quality of the point cloud data.
The quality detecting method of point cloud data mark quality according to an embodiment of the present invention, can obtain point cloud data, then basis
N number of disaggregated model that training obtains in advance respectively detects point cloud data, obtains point cloud data under N number of disaggregated model
Detect classification results, later the detection classification results by point cloud data under N number of disaggregated model respectively with the mark of point cloud data
Assessment is compared in data, N number of assessment score of point cloud data is obtained, finally according to N number of assessment score, to the mark of point cloud data
It infuses quality and carries out quality inspection.N number of testing result that point cloud data is detected respectively by N number of disaggregated model, and will be obtained
Assessment is compared with the labeled data of the point cloud data respectively, to obtain N number of assessment score of point cloud data, then basis is commented
Estimate score, reasonably the mark quality to point cloud data can carry out quality inspection, can effectively reduce " doubtful accidentally mark, spill tag " data
Range, and realize automation finds out problematic data, while can reduce human error, also reduce manually at
This.
In order to achieve the above objectives, the quality inspection device for the point cloud data mark quality that second aspect of the present invention embodiment proposes,
It include: data acquisition module, for obtaining point cloud data, wherein the point cloud data is the point cloud data by mark；Data
Detection module obtains described for being detected respectively to the point cloud data according to N number of disaggregated model that training obtains in advance
Detection classification results of the point cloud data under N number of disaggregated model, wherein N is positive integer；Data evaluation module, being used for will
Detection classification results of the point cloud data under N number of disaggregated model are carried out with the labeled data of the point cloud data respectively
Evaluation obtains N number of assessment score of the point cloud data；Quality testing module is used for according to N number of assessment score, to institute
The mark quality for stating point cloud data carries out quality inspection.
The quality inspection device of point cloud data mark quality according to an embodiment of the present invention, can obtain point cloud data, then basis
N number of disaggregated model that training obtains in advance respectively detects point cloud data, obtains point cloud data under N number of disaggregated model
Detect classification results, later the detection classification results by point cloud data under N number of disaggregated model respectively with the mark of point cloud data
Assessment is compared in data, N number of assessment score of point cloud data is obtained, finally according to N number of assessment score, to the mark of point cloud data
It infuses quality and carries out quality inspection.N number of testing result that point cloud data is detected respectively by N number of disaggregated model, and will be obtained
Assessment is compared with the labeled data of the point cloud data respectively, to obtain N number of assessment score of point cloud data, then basis is commented
Estimate score and quality inspection is carried out to the mark quality of point cloud data, it can be reasonable so as to pass through the assessment score of the point cloud data
Quality inspection is carried out to the mark quality of the point cloud data, can effectively reduce " doubtful accidentally mark, spill tag " data area, and realize certainly
Dynamicization finds out problematic data, while can reduce human error, also reduces cost of labor.
In order to achieve the above objectives, the computer equipment that third aspect present invention embodiment proposes, comprising: memory, processing
Device and it is stored in the computer program that can be run on the memory and on the processor, the processor executes the meter
When calculation machine program, the quality detecting method of the mark quality of point cloud data described in first aspect present invention embodiment is realized.
In order to achieve the above objectives, the computer readable storage medium that fourth aspect present invention embodiment proposes, the calculating
The quality inspection side of the mark quality of point cloud data described in first aspect present invention embodiment is realized when machine program is executed by processor
Method.
The additional aspect of the present invention and advantage will be set forth in part in the description, and will partially become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect and advantage of the invention will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, in which:
Fig. 1 is the flow chart of the quality detecting method of point cloud data mark quality according to an embodiment of the invention.
Fig. 2 is the flow chart of the quality detecting method of point cloud data mark quality accord to a specific embodiment of that present invention.
Fig. 3 is the structural schematic diagram of the quality inspection device of point cloud data mark quality according to an embodiment of the invention.
Fig. 4 is the structural schematic diagram of the quality inspection device of point cloud data mark quality in accordance with another embodiment of the present invention.
Fig. 5 is the structural schematic diagram of computer equipment according to an embodiment of the invention.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, it is intended to is used to explain the present invention, and is not considered as limiting the invention.
The explanation of nouns in the lower embodiment of the present invention is first introduced below:
Point cloud data refers to the set of one group of vector in a three-dimensional coordinate system, and point cloud data is several in addition to representative
Except what location information, RGB (Red-Green-Blue, three primary colors) color of a point also may indicate that, gray value, depth,
Segmentation result etc..The form that the point cloud data shows is equivalent to a 3D sample data, for example, image.Point cloud in the present invention
Data be with 3D laser radar the description that carries out of collected data instance.Wherein, which facilitates this field for being only
The understanding of technical staff, can not be as specific restriction of the invention.
In the related technology, it when the mark quality to point cloud data carries out quality inspection, is generally seen by artificial Quality Inspector's naked eyes
It examines and judges whether there is marking error.However, this by way of artificial quality inspection, may because of data volume big, artificial carelessness
Etc. reasons there is human error, that is, there is the case where " accidentally doubtful mark, spill tag ", and higher cost.
For this purpose, the present invention can the invention proposes the quality detecting method and its relevant device of a kind of point cloud data mark quality
Quality inspection automatically is carried out to point cloud data mark quality to realize, in such manner, it is possible to effectively reduce " doubtful accidentally mark, spill tag " number
According to range.Specifically, below with reference to the accompanying drawings describe the point cloud data mark quality detecting method of quality of the embodiment of the present invention, device,
Computer equipment and computer readable storage medium.
Fig. 1 is the flow chart of the quality detecting method of point cloud data mark quality according to an embodiment of the invention.It needs
Bright, the quality detecting method of the point cloud data mark quality of the embodiment of the present invention can be applied to the point cloud data of the embodiment of the present invention
Mark the quality inspection device of quality.The quality inspection device of point cloud data mark quality can be configured in computer equipment.For example, should
Computer equipment can be 3D scanning device, for example, the first-class hardware device of laser radar, stereo camera shooting.
As shown in Figure 1, the quality detecting method of point cloud data mark quality may include:
S110 obtains point cloud data.It should be noted that in an embodiment of the present invention, which is by mark
Point cloud data.Wherein, the mark of the point cloud data manually marks, and is also possible to using algorithm automatic marking, this hair
It is bright not limit this.
It should also be noted that, the point cloud data of the embodiment of the present invention can be the point cloud number in the sample pool pre-established
According to, for example, the point cloud data in the sample pool can be it is pre- first pass through scanning device different images carried out to various objects adopt
Obtained from collection, later, the point cloud data of acquisition can be labeled, and the point cloud data and its labeled data are stored
To establish the sample pool.
Optionally, the point cloud data of the embodiment of the present invention can be by carrying out with scanning device to some target object
The acquisition of live image later, can be labeled the point cloud data to be corresponded to obtaining the point cloud data of current scene
Labeled data.Wherein, which may include, but are not limited to: heap bulk measurement, Tunnel testing, highway mapping, bridge
Beam detection, confirmation of land right etc., so as to obtain the point cloud data under these scenes.
It should be noted that the scanning device can be laser radar, three-dimensional camera (Stereo Camera) is more crossed
Time camera (Time-of-flight Camera) equipment etc..
S120 respectively detects the point cloud data according to N number of disaggregated model that preparatory training obtains, obtains the cloud
Detection classification results of the data under N number of disaggregated model.Wherein, N is positive integer.
Optionally, the point cloud data is detected respectively according to N number of disaggregated model, obtains the point cloud data at N number of point
Detection classification results under class model.It is examined that is, the point cloud data can be separately input into N number of disaggregated model
It surveys, obtains the detection classification results of the point cloud data under each disaggregated model.For example, it is assumed that there are 10 disaggregated models, then can incite somebody to action
The point cloud data is separately input into 10 disaggregated models, and each disaggregated model carries out classification and Detection to the point cloud data, is obtained
To corresponding detection classification results.
It should be noted that training obtains N number of disaggregated model in advance.In one embodiment of the invention,
Trained in advance in the following manner it can obtain N number of disaggregated model:
S1) from the point cloud data sample pool marked, the sample of specified quantity is repeatedly randomly selected；
S2) according to the sample for the specified quantity randomly selected every time, the deep learning model being pre-created is carried out
Training, obtains N number of disaggregated model；Wherein, the point cloud data is randomly selected for model training every time not comprising described
Sample.
For example, based in the same point cloud data sample pool marked, such as 100,000 frame data, 1 is randomly selected every time
Ten thousand frame data.Training obtains multiple models under the same model, for example, extracting 10 times altogether, randomly selects 10,000 frame numbers every time
According to, it is each that the same model is trained using the data randomly selected, 10 models may finally be obtained.It can manage
Solution, the difference of N number of model also includes the randomness of data other than the randomness in model, for example, the model can have pair
Point cloud data is identified, identifies whether the function of having barrier.
It can be trained in advance from there through above-mentioned S1 and S2 and obtain N number of disaggregated model.It should be noted that in order to guarantee to mark
The fairness and accuracy of the quality inspection result of quality are infused, in an embodiment of the present invention, referring to the point cloud data for crossing model training
It is no longer participate in quality inspection sequence, that is to say, that the point cloud data for participating in quality inspection is the data for having neither part nor lot in model training.
S130, by detection classification results of the point cloud data under N number of disaggregated model respectively with the labeled data of point cloud data
Assessment is compared, obtains N number of assessment score of the point cloud data.
Optionally, using similarity measurements quantity algorithm, the point cloud data detection classification results under N number of disaggregated model point are calculated
Similarity factor value not between the labeled data of point cloud data, the then detection by point cloud data under N number of disaggregated model point
The class result similarity factor value between the labeled data of point cloud data respectively, is determined as N number of assessment score of point cloud data, should
Assessment score can be regarded as testing result and annotation results similarity score.Wherein, the similarity measurements quantity algorithm may include but
It is not limited to Distance conformability degree calculation method (such as Euclidean distance algorithm), cosine similarity algorithm, Jie Kade distance algorithm, specifically
Without limitation.
S140, according to N number of assessment score, the mark quality for calculating the point cloud data carries out quality inspection.
Optionally, according to N number of assessment score, the mean value and variance of point cloud data score are then calculated, the point cloud of judgement later
Whether the mean value of data score is less than first threshold, and judges whether the variance of point cloud data score is less than second threshold, if
Mean value is less than first threshold, and variance is less than second threshold, then determine the point cloud data is labeled as doubtful accidentally mark, spill tag.It needs
It is noted that in an embodiment of the present invention, first threshold and second threshold can be it is preset, need to can according to early period
It is determined depending on changing.
The quality detecting method of point cloud data mark quality according to an embodiment of the present invention, can obtain point cloud data, then basis
N number of disaggregated model that training obtains in advance respectively detects point cloud data, obtains point cloud data under N number of disaggregated model
Detect classification results, later the detection classification results by point cloud data under N number of disaggregated model respectively with the mark of point cloud data
Assessment is compared in data, N number of assessment score of point cloud data is obtained, finally according to N number of assessment score, to the mark of point cloud data
It infuses quality and carries out quality inspection.N number of testing result that point cloud data is detected respectively by N number of disaggregated model, and will be obtained
Assessment is compared with the labeled data of the point cloud data respectively, to obtain N number of assessment score of point cloud data, then basis is commented
Estimate score and quality inspection is carried out to the mark quality of point cloud data, it can be reasonable so as to pass through the assessment score of the point cloud data
Quality inspection is carried out to the mark quality of the point cloud data, can effectively reduce " doubtful accidentally mark, spill tag " data area, and realize certainly
Dynamicization finds out problematic data, while can reduce human error, also reduces cost of labor.
Fig. 2 is the flow chart of the quality detecting method of point cloud data mark quality accord to a specific embodiment of that present invention.It needs
It is noted that in every frame point cloud data, there may be multiple barriers, in order to investigate into a frame point cloud data barrier
Difficulty needs to combine the difficulty of each barrier in every frame.Specifically, as shown in Fig. 2, the point cloud data marks quality
Quality detecting method may include:
S210 obtains point cloud data, wherein the point cloud data is the point cloud data by mark.
S220 respectively detects point cloud data according to N number of disaggregated model that preparatory training obtains, obtains point cloud data
In detection classification results of each barrier under N number of disaggregated model.
Optionally, the point cloud data is detected respectively according to N number of disaggregated model, obtains each barrier in the point cloud data
Hinder detection classification results of the object under N number of disaggregated model.Wherein, N is positive integer.It should be noted that a frame point cloud data can
It is made of 2 parts, a part is unconcerned background, such as ground, lawn, vegetation, and another part is the prospect being concerned about, such as row
People, motor vehicle etc..And these prospects can be described as barrier, be the target for needing to detect.
That is, each disaggregated model is carried out classification and Detection to point cloud data, each barrier in the point cloud data is obtained
The detection classification results for hindering object, due to being N number of disaggregated model, so each barrier can correspond to obtain N number of detection classification results.
S230, calculate point cloud data in detection classification results of each barrier under the i-th disaggregated model, respectively with it is each
Similarity factor value between the labeled data of barrier.
In an embodiment of the present invention, similarity measurements quantity algorithm can be used, calculate each barrier in the point cloud data and exist
Detection classification results under the i-th disaggregated model similarity factor value between the labeled data of each barrier respectively,
In, 1≤i≤N.
For example, by taking similarity measurements quantity algorithm is Jie Kade distance algorithm as an example, hinder to investigate in every frame data
Hinder the difficulty of object, the difficulty of each barrier in every frame need to be combined, can be compared using similarity measurements quantity algorithm in every frame
The detected value (i.e. detection classification results) and mark value of each barrier, can be obtained a Jie Kade index (i.e. similarity factor
Value), the Jie Kade index value of each barrier indicates the testing result of the barrier and the similarity degree of annotation results, Jie Kade
The mark value registration of the bigger expression of index value and point cloud data is bigger.
As a kind of embodiment of possible implementation, by taking similarity measurements quantity algorithm is Euclidean distance algorithm as an example, in order to
The difficulty of barrier in every frame data is investigated, the difficulty of each barrier in every frame need to be combined, using similarity measurements
Quantity algorithm compares the detected value (i.e. detection classification results) and mark value of each barrier in every frame, can be obtained one it is European away from
From value, obtained Euclidean distance value is smaller, and the mark value similarity of the detected value and point cloud data that detect classification results is bigger,
Obtained Euclidean distance value is bigger, detects the detected value of classification results and the mark value similarity of point cloud data with regard to smaller.
S240, by detection classification results of the barrier each in point cloud data under the i-th disaggregated model, respectively with each barrier
Hinder the similarity factor value between the labeled data of object, is determined as assessment score of each barrier under the i-th disaggregated model.
That is, respectively and often by detection classification results of the barrier each in point cloud data under the i-th disaggregated model
The labeled data of a barrier is compared, and obtains a similarity factor value, wherein the similarity factor value is exactly each barrier
Assessment score under the i-th disaggregated model.
S250 obtains point cloud data at described i-th point according to assessment score of each barrier under the i-th disaggregated model
Assessment score under class model.
Optionally, assessment score of all barriers in the point cloud data under the i-th disaggregated model is ranked up, then
From all barriers after sequence in the assessment score under the i-th disaggregated model, quantile is chosen, and by the quantile of the selection
Determine assessment score of the point cloud data under the i-th disaggregated model.Wherein, in an embodiment of the present invention, the quantile can
To be median, it can also be other different quantiles, for example, quartile, decile etc..
That is, obtaining in a frame labeled data being ranked up, so after the similarity factor value of all barriers
The quantile for taking ranking results afterwards, as assessing for the testing result of the frame under the current model and annotation results similarity degree
Point.As a kind of embodiment in the cards, obtaining in a frame labeled data after the similarity factor value of all barriers, by it
It is ranked up, also can use the average value of ranking results, as assessment score of the frame under current i-th disaggregated model.
In an embodiment of the present invention, the middle position after sorting can be taken to N number of assessment score by being ranked up from big to small
Number, N number of assessment score as the point cloud data.Optionally, also N number of assessment score can be averaging or is weighted and ask flat
, N number of assessment score of the numerical value as the point cloud data will be obtained.
That is, first determining commenting for this frame point cloud data according to the assessment score of each barrier in 1 frame point cloud data
Score (being wherein under a model) is estimated, in this way, repeating n times can be obtained N number of assessment score of the point cloud data.
S260 carries out quality inspection to the mark quality of point cloud data according to N number of assessment score.
Optionally, in an embodiment of the present invention, according to N number of assessment score, the mean value of point cloud data score is then calculated
And variance, judge later the mean value of point cloud data score whether be less than first threshold and judge point cloud data score variance whether
Less than second threshold, if mean value is less than first threshold, and variance is less than second threshold, then determines being labeled as the point cloud data
Doubtful accidentally mark, spill tag.
For example, for obtaining 10 assessment scores, after obtaining 10 assessment scores, point cloud is calculated using formula
The mean value and variance of data score, judge whether the mean value of point cloud data score is less than first threshold later, judge point cloud data
Whether the variance of score is less than second threshold.If mean value is less than first threshold, and variance is less than second threshold, then determines the point
Cloud data are labeled as doubtful accidentally mark, spill tag, after determining doubtful accidentally mark, spill tag, that is, can be automated quality inspection.
Specifically, all score mean values are ranked up, the data that wherein mean value of score is lower and variance is relatively low
(i.e. all models can not all detect), as " doubtful accidentally mark, spill tag ".
The quality detecting method of point cloud data mark quality according to an embodiment of the present invention, obtains point cloud data, then according to N number of
Disaggregated model respectively detects the point cloud data, obtains in the point cloud data each barrier under N number of disaggregated model
Classification results are detected, calculate detection classification results difference of each barrier under the i-th disaggregated model in the point cloud data later
Similarity factor value between the labeled data of each barrier, then by barrier each in point cloud data in the i-th classification mould
Detection classification results under type, the similarity factor value between the labeled data of each barrier, determines each barrier respectively
Assessment score under i-th disaggregated model, the assessment score according to each barrier under the i-th disaggregated model, is obtained later
Assessment score of the point cloud data under i-th disaggregated model is taken, finally according to N number of assessment score, to the mark of point cloud data
Quality carries out quality inspection.Reasonably the mark quality to the point cloud data matter can be carried out by the assessment score of the point cloud data
Inspection can effectively reduce " accidentally doubtful mark, spill tag " data area, and realize automation finds out problematic data.
A kind of reality corresponding, of the invention with the point cloud data mark quality detecting method of quality that above-mentioned several embodiments provide
It applies example and a kind of quality inspection device of point cloud data mark quality is also provided, since point cloud data provided in an embodiment of the present invention marks matter
The quality inspection device of amount is corresponding with the point cloud data mark quality detecting method of quality that above-mentioned several embodiments provide, therefore in a cloud
The embodiment of the quality detecting method of data mark quality is also applied for the quality inspection of point cloud data mark quality provided in this embodiment
Device is not described in detail in the present embodiment.Fig. 3 is the matter of point cloud data mark quality according to an embodiment of the invention
The structural schematic diagram of checking device.
As shown in figure 3, the mark quality quality inspection device 300 of the point cloud data includes: data acquisition module 310, data inspection
Survey module 320, data evaluation module 330 and quality testing module 340, in which:
Data acquisition module 310 is for obtaining point cloud data, wherein the point cloud data is the point cloud number by mark
According to.
Data detection module 320 be used for according to the obtained N number of disaggregated model of training in advance respectively to the point cloud data into
Row detection, obtains detection classification results of the point cloud data under N number of disaggregated model, wherein N is positive integer.
Data evaluation module 330 is used for the detection classification results point by the point cloud data under N number of disaggregated model
Assessment is not compared with the labeled data of the point cloud data, obtains N number of assessment score of the point cloud data.
Quality testing module 340 is used for according to N number of assessment score, carries out quality inspection to the mark quality of the point cloud data.
The quality inspection device of point cloud data mark quality according to an embodiment of the present invention, can obtain point cloud data, then basis
N number of disaggregated model that training obtains in advance respectively detects point cloud data, obtains point cloud data under N number of disaggregated model
Detect classification results, later the detection classification results by point cloud data under N number of disaggregated model respectively with the mark of point cloud data
Assessment is compared in data, N number of assessment score of point cloud data is obtained, finally according to N number of assessment score, to the mark of point cloud data
It infuses quality and carries out quality inspection.N number of testing result that point cloud data is detected respectively by N number of disaggregated model, and will be obtained
Assessment is compared with the labeled data of the point cloud data respectively, to obtain N number of assessment score of point cloud data, then basis is commented
Estimate score and quality inspection is carried out to the mark quality of point cloud data, it can be reasonable so as to pass through the assessment score of the point cloud data
Quality inspection is carried out to the mark quality of the point cloud data, can effectively reduce " doubtful accidentally mark, spill tag " data area, and realize certainly
Dynamicization finds out problematic data, while can reduce human error, also reduces cost of labor.
It should be noted that training obtains N number of disaggregated model in advance.Optionally, in a reality of the invention
It applies in example, as shown in figure 4, the point cloud data marks quality quality inspection device further include: model training module 350 is for training in advance
Obtain N number of disaggregated model；Wherein, model training module 350 is specifically used for: from the point cloud data sample pool marked, repeatedly
Randomly select the sample of specified quantity；According to the sample for the specified quantity randomly selected every time, to the depth being pre-created
Learning model is trained, and obtains N number of disaggregated model；Wherein, point cloud data is randomly selected for model every time not comprising described
Trained sample.
It should be noted that there may be multiple barriers in every frame point cloud data, in order to investigate to a frame point cloud data
The difficulty of middle barrier needs to combine the difficulty of each barrier in every frame.Specifically, in one embodiment of the present of invention
In, data detection module 320 is specifically used for: being detected respectively to point cloud data according to N number of disaggregated model, obtains point cloud data
In detection classification results of each barrier under N number of disaggregated model.
In one embodiment of the invention, data evaluation module 330 includes: computing unit, for calculating point cloud data
In detection classification results of each barrier under the i-th disaggregated model, the phase between the labeled data of each barrier respectively
Like coefficient value, wherein 1≤i≤N；Determination unit, for the inspection by barrier each in point cloud data under the i-th disaggregated model
Classification results are surveyed, the similarity factor value between the labeled data of each barrier, is determined as each barrier at i-th point respectively
Assessment score under class model；Score acquiring unit, for the assessment score according to each barrier under the i-th disaggregated model,
Obtain assessment score of the point cloud data under i-th disaggregated model.
In one embodiment of the invention, score acquiring unit is specifically used for: existing to barriers all in point cloud data
Assessment score under i-th disaggregated model is ranked up；From assessment score of all barriers after sequence under the i-th disaggregated model
In, quantile is chosen, and the quantile of selection is determined into assessment score of the point cloud data under the i-th disaggregated model.
In one embodiment of the invention, quality testing module 340 is specifically used for calculating point cloud number according to N number of assessment score
According to the mean value and variance of score；Judge whether the mean value of point cloud data score is less than first threshold；Judge point cloud data score
Whether variance is less than second threshold；Mean value be less than first threshold, and variance be less than second threshold when, determine the mark of point cloud data
Note is doubtful accidentally mark, spill tag.
In order to realize above-described embodiment, the invention also provides a kind of computer equipments.
Fig. 5 is the structural schematic diagram of computer equipment according to an embodiment of the invention.As shown in figure 5, the server
500 may include: memory 510, processor 520 and be stored in the calculating that can be run on memory 510 and on processor 520
Machine program 530 when processor 520 executes program, realizes the mark quality of present invention point cloud data described in any of the above embodiments
Quality detecting method.
In order to realize above-described embodiment, the invention also provides a kind of computer program, the computer program is processed
Device realizes the quality detecting method of the mark quality of point cloud data described in any of the above embodiments when executing.
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is included at least one embodiment or example of the invention.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing specific logical function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the present invention includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, Lai Zhihang function, this should be of the invention
Embodiment person of ordinary skill in the field understood.
Expression or logic and/or step described otherwise above herein in flow charts, for example, being considered use
In the order list for the executable instruction for realizing logic function, may be embodied in any computer-readable medium, for
Instruction execution system, device or equipment (such as computer based system, including the system of processor or other can be held from instruction
The instruction fetch of row system, device or equipment and the system executed instruction) it uses, or combine these instruction execution systems, device or set
It is standby and use.For the purpose of this specification, " computer-readable medium ", which can be, any may include, stores, communicates, propagates or pass
Defeated program is for instruction execution system, device or equipment or the dress used in conjunction with these instruction execution systems, device or equipment
It sets.The more specific example (non-exhaustive list) of computer-readable medium include the following: there is the electricity of one or more wirings
Interconnecting piece (electronic device), portable computer diskette box (magnetic device), random access memory (RAM), read-only memory
(ROM), erasable edit read-only storage (EPROM or flash memory), fiber device and portable optic disk is read-only deposits
Reservoir (CDROM).In addition, computer-readable medium can even is that the paper that can print described program on it or other are suitable
Medium, because can then be edited, be interpreted or when necessary with it for example by carrying out optical scanner to paper or other media
His suitable method is handled electronically to obtain described program, is then stored in computer storage.
It should be appreciated that each section of the invention can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storage
Or firmware is realized.It, and in another embodiment, can be under well known in the art for example, if realized with hardware
Any one of column technology or their combination are realized: having a logic gates for realizing logic function to data-signal
Discrete logic, with suitable combinational logic gate circuit specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
Those skilled in the art are understood that realize all or part of step that above-described embodiment method carries
It suddenly is that relevant hardware can be instructed to complete by program, the program can store in a kind of computer-readable storage medium
In matter, which when being executed, includes the steps that one or a combination set of embodiment of the method.
It, can also be in addition, each functional unit in each embodiment of the present invention can integrate in a processing module
It is that each unit physically exists alone, can also be integrated in two or more units in a module.Above-mentioned integrated mould
Block both can take the form of hardware realization, can also be realized in the form of software function module.The integrated module is such as
Fruit is realized and when sold or used as an independent product in the form of software function module, also can store in a computer
In read/write memory medium.
Storage medium mentioned above can be read-only memory, disk or CD etc..Although having been shown and retouching above
The embodiment of the present invention is stated, it is to be understood that above-described embodiment is exemplary, and should not be understood as to limit of the invention
System, those skilled in the art can be changed above-described embodiment, modify, replace and become within the scope of the invention
Type.
Claims (14)
1. a kind of quality detecting method of point cloud data mark quality characterized by comprising
Obtain point cloud data, wherein the point cloud data is the point cloud data by mark；
The point cloud data is detected respectively according to N number of disaggregated model that preparatory training obtains, obtains the point cloud data
Detection classification results under N number of disaggregated model, wherein N is positive integer；
By detection classification results of the point cloud data under N number of disaggregated model respectively with the mark number of the point cloud data
According to assessment is compared, N number of assessment score of the point cloud data is obtained；
According to N number of assessment score, quality inspection is carried out to the mark quality of the point cloud data.
2. the method according to claim 1, wherein training obtains N number of classification in advance in the following manner
Model:
From the point cloud data sample pool marked, the sample of specified quantity is repeatedly randomly selected；
According to the sample for the specified quantity randomly selected every time, the deep learning model being pre-created is trained, is obtained
To N number of disaggregated model；Wherein, the point cloud data does not include the sample randomly selected every time for model training.
3. the method according to claim 1, wherein right respectively according to N number of disaggregated model that preparatory training obtains
The point cloud data is detected, and detection classification results of the point cloud data under N number of disaggregated model are obtained, comprising:
The point cloud data is detected respectively according to N number of disaggregated model, obtains each obstacle in the point cloud data
Detection classification results of the object under N number of disaggregated model.
4. according to the method described in claim 3, it is characterized in that, by the point cloud data under N number of disaggregated model
Assessment is compared with the labeled data of the point cloud data respectively in detection classification results, obtains the N number of of the point cloud data and comments
Estimate score, comprising:
Calculate detection classification results of each barrier under the i-th disaggregated model in the point cloud data, respectively with it is described each
Similarity factor value between the labeled data of barrier, wherein 1≤i≤N；
By detection classification results of the barrier each in the point cloud data under i-th disaggregated model, respectively with it is described every
Similarity factor value between the labeled data of a barrier is determined as each barrier under i-th disaggregated model
Assess score；
According to assessment score of each barrier under i-th disaggregated model, the point cloud data is obtained described i-th
Assessment score under disaggregated model.
5. according to the method described in claim 4, it is characterized in that, according to each barrier in i-th disaggregated model
Under assessment score, obtain assessment score of the point cloud data under i-th disaggregated model, comprising:
Assessment score of all barriers in the point cloud data under i-th disaggregated model is ranked up；
From all barriers after sequence in the assessment score under i-th disaggregated model, quantile is chosen, and by the choosing
The quantile taken determines assessment score of the point cloud data under i-th disaggregated model.
6. the method according to claim 1, wherein according to N number of assessment score, to the point cloud data
It marks quality and carries out quality inspection, comprising:
According to N number of assessment score, the mean value and variance of the point cloud data score are calculated；
Judge whether the mean value of the point cloud data score is less than first threshold；
Judge whether the variance of the point cloud data score is less than second threshold；
If the mean value is less than the first threshold, and the variance is less than second threshold, then determines the point cloud data
It is labeled as doubtful accidentally mark, spill tag.
7. a kind of quality inspection device of point cloud data mark quality characterized by comprising
Data acquisition module, for obtaining point cloud data, wherein the point cloud data is the point cloud data by mark；
Data detection module, for being detected respectively to the point cloud data according to N number of disaggregated model that training obtains in advance,
Obtain detection classification results of the point cloud data under N number of disaggregated model, wherein N is positive integer；
Data evaluation module, for by detection classification results of the point cloud data under N number of disaggregated model respectively with institute
Assessment is compared in the labeled data for stating point cloud data, obtains N number of assessment score of the point cloud data；
Quality testing module, for carrying out quality inspection to the mark quality of the point cloud data according to N number of assessment score.
8. device according to claim 7, which is characterized in that further include:
Model training module obtains N number of disaggregated model for training in advance；
Wherein, the model training module is specifically used for:
From the point cloud data sample pool marked, the sample of specified quantity is repeatedly randomly selected；
According to the sample for the specified quantity randomly selected every time, the deep learning model being pre-created is trained, is obtained
To N number of disaggregated model；Wherein, the point cloud data does not include the sample randomly selected every time for model training.
9. device according to claim 7, which is characterized in that the data detection module is specifically used for:
The point cloud data is detected respectively according to N number of disaggregated model, obtains each obstacle in the point cloud data
Detection classification results of the object under N number of disaggregated model.
10. device according to claim 9, which is characterized in that the data evaluation module includes:
Computing unit, for calculating detection classification results of each barrier under the i-th disaggregated model in the point cloud data, point
Similarity factor value not between the labeled data of each barrier, wherein 1≤i≤N；
Determination unit, for the detection classification results by barrier each in the point cloud data under i-th disaggregated model,
Similarity factor value between the labeled data of each barrier respectively is determined as each barrier described i-th
Assessment score under disaggregated model；
Score acquiring unit, for the assessment score according to each barrier under i-th disaggregated model, described in acquisition
Assessment score of the point cloud data under i-th disaggregated model.
11. device according to claim 10, which is characterized in that the score acquiring unit is specifically used for:
Assessment score of all barriers in the point cloud data under i-th disaggregated model is ranked up；
From all barriers after sequence in the assessment score under i-th disaggregated model, quantile is chosen, and by the choosing
The quantile taken determines assessment score of the point cloud data under i-th disaggregated model.
12. device according to claim 7, which is characterized in that the quality testing module is specifically used for:
According to N number of assessment score, the mean value and variance of the point cloud data score are calculated；
Judge whether the mean value of the point cloud data score is less than first threshold；
Judge whether the variance of the point cloud data score is less than second threshold；
The mean value be less than the first threshold, and the variance be less than second threshold when, determine the mark of the point cloud data
Note is doubtful accidentally mark, spill tag.
13. a kind of computer equipment characterized by comprising memory, processor and be stored on the memory and can be
The computer program run on the processor when the processor executes the computer program, is realized according to claim 1
To the quality detecting method of the mark quality of point cloud data described in any one of 6.
14. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The quality detecting method of point cloud data mark quality according to any one of claim 1 to 6 is realized when being executed by processor.
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