CN109492685A - A kind of target object visible detection method for symmetrical feature - Google Patents
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Abstract
The invention discloses a kind of target object visible detection methods for symmetrical feature, specific steps are as follows: there is the image of target object to carry out processing shooting and obtain multiple foundation characteristics, then candidate frame is constituted, ascending order arrangement is carried out along X-axis according to the position of multiple foundation characteristics in the picture, then ascending order direction and its two nearest feature are only matched to the foundation characteristic in sequence, separately constitute candidate frame, then classified using the image classification model that machine learning or the training of deep learning algorithm obtain to multiple candidate frames, for same N number of foundation characteristic, algorithm complexity of the invention is up to 2N-3, its algorithm complexity is less than ergodic algorithm complexity (N-1) two-by-two always2, therefore method complexity of the invention is lower, required detection time is shorter, to substantially increase the detection efficiency of target object.
Description
Technical field
The present invention relates to a kind of target object visible detection method, specifically a kind of object for symmetrical feature is stereoscopic
Feel detection method.
Background technique
Target detection is one of three big tasks of computer vision field, i.e., shoots ambient enviroment and generate image, then
Shooting image is identified, obtains in shooting image with the presence or absence of the target pattern being preset in computer, is extracted
Come, completes the detection identification process to target pattern in environment.The algorithm of target detection of mainstream is mainly based upon depth at present
Model is practised, one of major class is exactly two-stage detection algorithm, i.e., test problems is divided into two stages, clapped first
It takes the photograph in image and generates multiple candidate regions, then classify to each candidate region, finally each candidate region identify
Object detection results out, Typical Representative such as R-CNN serial algorithm etc..
For two-stage detection algorithm, the generation of candidate frame is calculated by traditional machine vision at present
Method carries out pretreatment to image and obtains foundation characteristic according to the detection some more basic features being readily detected of object.But
When detection has the target object of symmetrical basic feature, after often being traversed to each foundation characteristic detected, two
Then two combination producing candidate frames are again classified to candidate frame using the method for machine learning or deep learning to it.Assuming that one
Width shooting image has N number of foundation characteristic, then the complexity of matching algorithm is (N-1) two-by-two2.It will lead to algorithm complexity in this way
Higher, time cost is too high, and final object detection results are also easy to be interfered, such as in the picture for two phases
Foundation characteristic when farther out or especially close is It is not necessary to which carrying out matching generates candidate frame.Therefore it is existing this
Kind mode algorithm complexity is high, and required detection time is longer, therefore the efficiency of target detection is lower.
Summary of the invention
In view of the above existing problems in the prior art, the present invention provides a kind of target object vision inspections for symmetrical feature
Survey method has complexity lower, and required detection time is shorter, can improve the detection efficiency of target object.
To achieve the goals above, the technical solution adopted by the present invention is that: a kind of object for symmetrical feature is stereoscopic
Feel detection method, specific steps are as follows:
(1) the image input computer of multiple target objects is acquired, then hand labeled goes out to acquire target object in image
And multiple distracters, it is fabricated to data set, the class wherein determining target object is positive, remaining class that is negative；Computer uses known depth
Degree learning method or known machine learning method determine image classification model, will instruct after data set input picture disaggregated model
Practice, the image classification model after finally obtaining training, and is saved；
(2) image is shot to the environment of required detection, by shooting image using known machine visual processing method to image
It is pre-processed, obtains multiple symmetrical basic features in shooting image, and carry out using XY axis coordinate system to each foundation characteristic
Then mark obtains foundation characteristic sequence L0；
(3) ascending order arrangement is done according to its X axis coordinate position in the picture to each foundation characteristic, obtains foundation characteristic sequence
Arrange L1；
(4) in foundation characteristic sequence L1, from low to high to each foundation characteristic, the foundation characteristic is found out along X axis coordinate
The nearest foundation characteristic in two, ascending order direction, and the foundation characteristic and two nearest foundation characteristics is made to separately constitute two candidates
Frame obtains one group of candidate frame, and so on, the candidate frame group of each foundation characteristic is obtained, wherein in last group of candidate frame group
For a candidate frame, two candidate frames in remaining every group, lower according to X axis coordinate sequence is H0, another is H1；By each group
Candidate frame is saved as sequence L2；
(5) successively candidate frame H0 in each group in sequence L2 is judged using preset candidate frame threshold range,
If the candidate frame H0 detected in current group is not in threshold range, candidate frame H0 is rejected, in the group
Candidate frame H1 repeat above-mentioned deterministic process and reject candidate frame H1 if being not in threshold range, continue next group of candidate
The detection of frame；If carrying out classification judgement in threshold range to candidate frame H1 using image classification model, completing classification
Continue the detection of next group of candidate frame afterwards；
If the candidate frame H0 detected in current group is in threshold range, using image classification obtained in step (1)
Model carries out classification judgement to this group of candidate frame H0,
The class if classification results are positive directly determines candidate frame H0 and is positive class, rejects candidate frame H1 in the group, then carry out
The detection of next group of candidate frame；
The class if classification results are negative repeats above-mentioned deterministic process to the candidate frame H1 in the group, if being not at threshold value model
In enclosing, then candidate frame H1 is rejected, the detection of next group of candidate frame is continued；If using image classification in threshold range
Model carries out classification judgement to candidate frame H1, completes the detection for continuing next group of candidate frame after classifying；
Until all candidate frame groups complete detect and classify after, using classification results be positive class candidate frame as sequence L3
It saves；
(6) each candidate frame is to shoot all target objects to be detected in image in sequence L3.
Further, image is pre-processed in the step (2), including gray proces, binary conversion treatment, edge
Reason, shape and color detection.
Further, known deep learning method is CNN deep learning method in the step (1)；Known machine study side
Method is SVM machine learning method or KNN machine learning method.
Further, preset candidate frame threshold range includes two for setting composition candidate frame in the step (5)
Foundation characteristic respectively presss from both sides between the threshold range or setting candidate frame adjacent long edges and short side of the ratio between long side of minimum area-encasing rectangle
The threshold range at angle.
Compared with prior art, the present invention combines mode using machine learning image classification and two-stage detection, right
Shooting, there is the image of target object, which to carry out processing, obtains multiple foundation characteristics, then constitutes candidate frame, special according to multiple bases
Sign position in the picture carries out ascending order arrangement along X-axis, then to the foundation characteristic in sequence only match ascending order direction and its most
Two close features, separately constitute candidate frame, the image point then obtained using machine learning or the training of deep learning algorithm
Class model classifies to multiple candidate frames, and for same N number of foundation characteristic, algorithm complexity of the invention is up to 2N-3,
Its algorithm complexity is less than ergodic algorithm complexity (N-1) two-by-two always2, therefore method complexity of the invention is lower, institute
The detection time needed is shorter, to substantially increase the detection efficiency of target object.
Detailed description of the invention
Fig. 1 is the target object schematic diagram in the embodiment of the present invention with symmetrical basic feature；
Fig. 2 is that the candidate frame of one of shooting image of the invention generates schematic diagram；
Fig. 3 is that the candidate frame of another shooting image of the invention generates schematic diagram.
Specific embodiment
The present invention will be further described below.
Embodiment:
As shown, of the invention: specific steps are as follows:
(1) the image input computer of multiple target objects is acquired, then hand labeled goes out to acquire target object in image
And multiple distracters, it is fabricated to data set, the class wherein determining target object (as shown in Figure 1) is positive, remaining class that is negative；Computer
Image classification model is determined using known deep learning method or known machine learning method, by data set input picture classification mould
It is trained, the image classification model after finally obtaining training, and is saved after type；
(2) image (as shown in Figures 2 and 3) is shot to the environment of required detection, shooting image is regarded using known machine
Feeling that processing method carries out pretreatment to image includes gray proces, binary conversion treatment, edge processing, shape and color detection, is obtained
Four symmetrical basic features into shooting image, and each foundation characteristic is labeled using XY axis coordinate system, along X-axis ascending order
Direction is respectively T0, T1, T2, T3, then obtains foundation characteristic sequence L0；
(3) ascending order arrangement is done according to its X axis coordinate position in the picture to each foundation characteristic, obtains foundation characteristic sequence
Arrange L1；
(4) in foundation characteristic sequence L1, from low to high to each foundation characteristic, the foundation characteristic is found out along X axis coordinate
The nearest foundation characteristic in two, ascending order direction, and the foundation characteristic and two nearest foundation characteristics is made to separately constitute two candidates
Frame obtains one group of candidate frame, and so on, obtain candidate frame group (the i.e. foundation characteristic T0 and it is nearest of each foundation characteristic
Two foundation characteristics T1 and T2 separately constitute candidate frame CDJI and candidate frame CDFE, form one group of candidate frame；Foundation characteristic T1 with
Its nearest two foundation characteristics T2 and T3 separately constitute candidate frame EFJI and candidate frame EFGH, form one group of candidate frame), wherein
For a candidate frame, (i.e. foundation characteristic T2 only has the last one foundation characteristic on ascending order direction in last group of candidate frame group
T3 forms candidate frame IJGH), two candidate frames in remaining every group, lower according to X axis coordinate sequence is H0, another is H1
(candidate frame CDJI is less than candidate frame CDFE in its respective maximum X axis coordinate of such as candidate frame CDJI and candidate frame CDFE, it is determined that
Candidate frame CDJI is the candidate frame H0 in group, and candidate frame CDFE is the candidate frame H1 in group)；Using each group candidate frame as sequence L2
It saves；
(5) using the threshold for presetting respective the ratio between the long side of minimum area-encasing rectangle of two foundation characteristics of composition candidate frame
Being worth range is [0.5,1.5], and set the threshold range of angle between candidate frame adjacent long edges and short side as [80 °, 100 °] according to
It is secondary that candidate frame H0 in each group in sequence L2 is judged:
As shown in Fig. 2, (i.e. candidate frame CDEF) is detected to the candidate frame in first group being H0, through detecting candidate frame
CDEF is not between candidate frame adjacent long edges and short side in the threshold range of angle, then is rejected the candidate frame, then to this
Candidate frame H1 (i.e. candidate frame CDIJ) in group is detected, the threshold value in angle between candidate frame adjacent long edges and short side
In range, classified using image classification model obtained in step (1) to candidate frame CDJI, classification results are candidate frame
CDJI is positive class (comprising target object i.e. in candidate frame), then carries out the detection of next group of candidate frame；
Second group of candidate frame H0 is detected (i.e. candidate frame EFJI) and is repeated the above process, and is detected candidate frame EFJI and not located
Between candidate frame adjacent long edges and short side in the threshold range of angle, then the candidate frame is rejected, then to the time in the group
It selects frame H1 (i.e. candidate frame EFGH) to be detected, is between candidate frame adjacent long edges and short side in the threshold range of angle,
Classified using image classification model obtained in step (1) to candidate frame EFGH, classification results are that candidate frame EFGH is positive
Then class carries out the detection of next group of candidate frame；
The last one candidate frame JIGH is detected, through detect candidate frame JIGH be not at candidate frame adjacent long edges with it is short
Between side in the threshold range of angle, then the candidate frame is rejected；Finally classification results are positive candidate frame (the i.e. candidate frame of class
CDJI and candidate frame EFGH) it is saved as sequence L3；
As shown in figure 3, (i.e. candidate frame CDJI) is detected to the candidate frame in first group being H0, through detecting candidate frame
CDJI is between candidate frame adjacent long edges and short side in the threshold range of angle, using image classification obtained in step (1)
Model classifies to candidate frame CDJI, and classification results are that candidate frame CDJI is positive class (comprising target object i.e. in candidate frame),
Since candidate frame CDJI is candidate frame H0 in group, the candidate frame H1 (i.e. candidate frame CDFE) in the group is directly removed,
Then the detection of next group of candidate frame is carried out；
Second group of candidate frame H0 is detected (i.e. candidate frame EFJI) and is repeated the above process, and show that candidate frame EFJI is negative
Class, candidate frame EFGH are candidate frame H0 in group, the candidate frame H1 (i.e. candidate frame IJGH) due to its class that is negative, in the group
Above-mentioned detection and assorting process are repeated, show that candidate frame IJGH is positive class；
Then detection and assorting process are carried out to the last one candidate frame EFGH, show that candidate frame EFGH is positive class；Finally
By classification results be positive class candidate frame (i.e. candidate frame CDIJ, candidate frame JIGH and candidate frame EFGH) as sequence L3 save；
(6) each candidate frame is to shoot all target objects to be detected in image in sequence L3.
Further, known deep learning method is CNN deep learning method in the step (1)；Known machine study side
Method is SVM machine learning method or KNN machine learning method.
Claims (4)
1. a kind of target object visible detection method for symmetrical feature, which is characterized in that specific steps are as follows:
(1) the image input computer of multiple target objects is acquired, then hand labeled goes out to acquire target object and more in image
A distracter, is fabricated to data set, the class wherein determining target object is positive, remaining class that is negative；Computer uses known depth
Learning method or known machine learning method determine image classification model, will be trained after data set input picture disaggregated model,
Image classification model after finally obtaining training, and saved；
(2) image is shot to the environment of required detection, shooting image carries out image using known machine visual processing method
Pretreatment is obtained multiple symmetrical basic features in shooting image, and is labeled to each foundation characteristic using XY axis coordinate system,
Then foundation characteristic sequence L0 is obtained；
(3) ascending order arrangement is done according to its X axis coordinate position in the picture to each foundation characteristic, obtains foundation characteristic sequence
L1；
(4) in foundation characteristic sequence L1, from low to high to each foundation characteristic, the foundation characteristic is found out along X axis coordinate ascending order
The nearest foundation characteristic in two, direction, and the foundation characteristic and two nearest foundation characteristics is made to separately constitute two candidate frames,
One group of candidate frame is obtained, and so on, the candidate frame group of each foundation characteristic is obtained, is wherein one in last group of candidate frame group
A candidate frame, two candidate frames in remaining every group, lower according to X axis coordinate sequence is H0, another is H1；By each group candidate
Frame is saved as sequence L2；
(5) successively candidate frame H0 in each group in sequence L2 is judged using preset candidate frame threshold range,
If the candidate frame H0 detected in current group is not in threshold range, candidate frame H0 is rejected, to the time in the group
It selects frame H1 to repeat above-mentioned deterministic process and rejects candidate frame H1 if being not in threshold range, continue next group of candidate frame
Detection；If carry out classification judgement in threshold range to candidate frame H1 using image classification model, it is subsequent to complete classification
Continue the detection of next group of candidate frame；
If the candidate frame H0 detected in current group is in threshold range, using image classification model obtained in step (1)
Classification judgement is carried out to this group of candidate frame H0,
The class if classification results are positive directly determines candidate frame H0 and is positive class, rejects candidate frame H1 in the group, then carry out next
The detection of group candidate frame；
The class if classification results are negative repeats above-mentioned deterministic process to the candidate frame H1 in the group, if being not in threshold range,
Candidate frame H1 is then rejected, the detection of next group of candidate frame is continued；If using image classification model pair in threshold range
Candidate frame H1 carries out classification judgement, completes the detection for continuing next group of candidate frame after classifying；
Until all candidate frame groups complete detect and classify after, using classification results be positive class candidate frame as sequence L3 guarantor
It deposits；
(6) each candidate frame is to shoot all target objects to be detected in image in sequence L3.
2. a kind of target object visible detection method for symmetrical feature according to claim 1, which is characterized in that institute
It states and image is pre-processed in step (2), including gray proces, binary conversion treatment, edge processing, shape and color detection.
3. a kind of target object visible detection method for symmetrical feature according to claim 1, which is characterized in that institute
Stating known deep learning method in step (1) is CNN deep learning method；Known machine learning method is the machine learning side SVM
Method or KNN machine learning method.
4. a kind of target object visible detection method for symmetrical feature according to claim 1, which is characterized in that institute
State the respective most parcel of two foundation characteristics that preset candidate frame threshold range in step (5) includes setting composition candidate frame
Enclose the threshold range of angle between the threshold range or setting candidate frame adjacent long edges and short side of the ratio between long side of rectangle.
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