CN105022990B - A kind of waterborne target rapid detection method based on unmanned boat application - Google Patents
A kind of waterborne target rapid detection method based on unmanned boat application Download PDFInfo
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Abstract
The invention discloses a kind of waterborne target rapid detection methods based on unmanned boat application, belong to Digital Image Processing and control system interleaving techniques field.The present invention analyzes to obtain object candidate area by Objective, due to that can have certain false-alarm in candidate region, obtains marking area using significance analysis, and Objective is combined with conspicuousness, rejects false-alarm, obtains target accurate location.The present invention is without specific objective type information, therefore universality is preferable, compared to existing other algorithm of target detection, either in terms of the detection result of target, or all has greatly improved in terms of the speed of method, there is important directive significance to the automatic obstacle-avoiding of unmanned boat.
Description
Technical field
The invention belongs to Digital Image Processing and control system interleaving techniques field, and nothing is based on more particularly, to one kind
The waterborne target rapid detection method of people's ship application.
Background technology
The strong interest of numerous ocean military powers is caused about the research and development of unmanned boat recent years, wherein
More representational is the U.S. " Sparta " number unmanned boat and Israel " Protector " number unmanned boat.Currently, either from
Civilian or military angle is set out, and demand of the China to unmanned boat is all increasing increasingly, this territorial waters make an inspection tour, strike pirate and
The fields such as smuggling seem particularly urgent.In the autonomous navigation of unmanned boat, the quick detection of waterborne target is that unmanned boat is kept away automatically
The basis of barrier.Several currently used object detection methods are described below：
(1) it is based on the matched object detection method of local feature
Target and image to be detected are usually passed through into key point and key point based on the matched target detection of local feature
Information describes target in neighborhood, or describes target by the characteristic information in regional area.
2004, David Lowe proposed famous SIFT (Scale-Invariant Feature on IJCV
Transform) local feature description's, can effectively adapt to the influence that the variations such as scale, rotation, affine and visual angle are brought.It should
The difference that algorithm is filtered by image pyramid and Gaussian kernel, detects the extreme point in Laplacian space as feature
Point, and be described by 128 dimensional features of part, make it that there is better adaptability and robustness in application.
(2) structure-based object detection method
Object structures can be very good to reflect target information.Usual object is made of structured features, such as people
It is usually made of head, trunk and four limbs, face is usually made of face, and vehicle is usually made of vehicle body and wheel.This structuring
Information can accurately detected target from complex scene.
2010, Pedro Felzenszwalb proposed DPM models on PAMI.Target is divided into several by DPM models
Different components judges that the object is not according to the position relationship between the matching degree and component of different components when detecting
It is the target for needing to detect.DPM is current best one of algorithm of target detection, and obtains the detection hat on VOC for successive years
Army.
(3) algorithm of target detection based on deep learning
The concept of deep learning is derived from the research of artificial neural network.Deep learning is formed more by combining low-level feature
Abstract high-rise expression attribute classification or feature, to find that the distributed nature of data indicates.CNN is also convolutional neural networks,
It is a kind of current most commonly used deep learning model.
Ross Girshick propose R-CNN methods on CVPR within 2014, and object candidate area and CNN are combined
Come, is used for target detection.Target detection is divided into two parts by R-CNN：Find object candidate area and target identification.R-CNN
The full articulamentum of CNN structures is substituted for SVM classifier, and feature extraction is used for using the first half of CNN structures.R-CNN
Very good effect is obtained in target detection neighborhood, also becomes an important branch of target detection neighborhood.
Although at present there are many algorithm of target detection, either feature based matching, DPM algorithms is based on R-
CNN algorithms all have that universality is poor.It is more effective to the detection of simple target, such as only detects certain one kind
The ship of type.And in the autonomous navigation of unmanned boat, the target type that is faced numerous (such as pleasure boat, sailing boat, warship, buoy, drifts
Float, reef etc.), and the posture of target, view transformation are all very big, therefore current algorithm of target detection cannot be fine
The true natural scene of adaptation.Additionally, due to unmanned boat towards practical application, thus it is relatively high to the requirement of real-time of algorithm,
And current DPM, R-CNN algorithm complexity is too high, the more difficult satisfaction of real-time.
In conclusion although at present there are many related algorithm in terms of target detection, all because of algorithm universality and
The reasons such as complexity, it is difficult to apply it in the automatic obstacle-avoiding of unmanned boat.
Invention content
For the disadvantages described above or Improvement requirement of the prior art, the present invention provides a kind of water surface mesh applied based on unmanned boat
Rapid detection method is marked, to realize the automatic obstacle-avoiding and autonomous navigation of unmanned boat.The present invention is without any specific objective type
Information, therefore universality is preferable.Inventive algorithm complexity is relatively low simultaneously, can detect and be encountered in unmanned boat autonomous navigation in real time
Various barriers.
The present invention provides a kind of waterborne target rapid detection method applied based on unmanned boat, includes the following steps：
Step 1 trains grader and interlayer grader in layer, wherein grader is used for empty in the scale of structure in the layer
Between each layer judge whether current candidate region is target area, the interlayer grader is based on the weighting between different layers
It calculates；
Step 2 carries out Objective analysis using grader in the layer and the interlayer grader to original image, obtains
Final object candidate area, including following sub-step：
(2-1) carries out change of scale to the original image, builds pyramid model, obtains the figure of different scale size
Picture is denoted as L1, L2..., LM, wherein M indicates the number of plies of the scale space of structure；
(2-2) is in each tomographic image LiIn, the area of fixed size is extracted to each position using the method for sliding window
Domain calculates the NG features in the region, and by the score value in the classifier calculated region in the layer, obtains the target of different layers
Candidate region；
(2-3) is weighted marking, and root by the interlayer grader to the object candidate area obtained in different layers
It is ranked up according to weighting marking result；
(2-4) carries out maximum inhibition to object candidate area, and obtains the final object candidate area；
Step 3 trains random forest to return device and Multiscale Fusion weight, wherein the random forest returns device based on
Point counting cuts the significance value of each rear super-pixel block, and the Multiscale Fusion weight is used to merge to be obtained under different scale
Saliency maps；
Step 4 returns device and the Multiscale Fusion weight using the random forest and is carried out significantly to the original image
Property analysis, obtain final Saliency maps；
Step 5 will include a large amount of false-alarms according to the final object candidate area and the final Saliency maps
Candidate region is rejected, and the accurate location of target is finally obtained.
In general, through the invention it is contemplated above technical scheme is compared with the prior art, have below beneficial to effect
Fruit：
The present invention can quickly detect the various barriers encountered in unmanned boat autonomous navigation.By handling unmanned boat
The image of upper video camera shooting, real-time perception surrounding enviroment realize the autonomous navigation of unmanned boat.To the image of camera acquisition,
Object candidate area is obtained by Objective, and notable sex knowledge is combined to reject target false-alarm.The present invention compared to it is existing its
Its algorithm of target detection either in terms of the detection result of target, or all has greatly improved in terms of the speed of algorithm,
There is important directive significance to the automatic obstacle-avoiding of unmanned boat.
Description of the drawings
Fig. 1 is the flow chart for the waterborne target rapid detection method applied the present invention is based on unmanned boat；
Fig. 2 is the flow chart of detection-phase Objective of the present invention analysis；
Fig. 3 is the result figure of the invention by being obtained after Objective algorithm process；
Fig. 4 is the flow chart of detection-phase significance analysis of the present invention；
Fig. 5 is the result figure of detection-phase significance analysis of the present invention.
Specific implementation mode
In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in the various embodiments of the present invention described below
It does not constitute a conflict with each other and can be combined with each other.
The present invention is divided into three parts：First, training objective model and using trained model to image to be detected
Objective analysis is carried out, obtains object candidate area, object candidate area at this time can have certain false-alarm；Then, training
Conspicuousness model simultaneously carries out significance analysis using trained model to image to be detected, obtains Saliency maps；Finally, by mesh
Mark property is combined with conspicuousness, rejects target false-alarm.
Fig. 1 show the flow chart for the waterborne target rapid detection method applied the present invention is based on unmanned boat, specifically includes
Following steps：
Step 1 training objective model.This training stage mainly reaches two purposes：Grader, training layer in training layer
Between grader.Grader is for judging whether current candidate region is target area at each layer in its middle level；Interlayer grader
For the weighted calculation between different layers.In embodiments of the present invention, PASCAL VOC 2007 are used as and train by the training stage
Collection, wherein comprising 10000 figures, wherein 5000 are used for training, 5000 are used for testing.Step 1 specifically includes following sub-step
Suddenly：
Grader in (1-1) training layer, directly extracts target area, and be compressed into fixed size to training set sample
Block, you can as positive sample, in embodiments of the present invention, the unified block using 8 × 8 sizes.Training set sample is randomly selected
Candidate blocks, as long as candidate blocks are overlapping with target area to can be used as negative sample less than fixed threshold, in embodiments of the present invention,
Fixed threshold is selected as 50%；
(1-2) training interlayer grader, carries out training set sample the adjustment of scale, obtains the image of different layers, at random
The block for taking 8 × 8 sizes, reverts to according to compression factor under original graph, if it is Chong Die with target area be more than 50% if as just
Otherwise sample is used as negative sample.
Step 2 carries out Objective analysis, Objective analysis using trained Objective model in step 1 to original image
It is a kind of method quickly obtaining object candidate area.Fig. 2 show the flow chart of detection-phase Objective analysis of the present invention, tool
Body includes following sub-step：
(2-1) carries out change of scale to original image, builds pyramid model, obtains the image of different scale size, if
For L1, L2..., LM, the number of plies of the scale space of wherein M expression structures, in embodiments of the present invention, M takes 33；
(2-2) is in each tomographic image LiIn (i=1,2, M), by the way of sliding window, to each position extraction 8
The region of × 8 sizes, and normalized gradient (Normed Gradients, the hereinafter referred to as NG) feature in the region is calculated, and lead to
The score value in the classifier calculated region in layer is crossed, which is used for measuring the possibility that the position is object candidate area,
It can be obtained by the object candidate area of different layers in this way.In embodiments of the present invention, it when calculating NG features, takes in all channels
Horizontal direction maximum of gradients is gx, vertical gradient maximum value is gy, and by formula min (| gx|+|gy|, 255) it counts
Calculate the characteristic value of each point；
(2-3) by step (2-2) processing after, for each tomographic image LiEach position candidate can there are one
Score value, for measuring the possibility that the position is object candidate area.In view of constructing many layers in the present invention, so passing through
Interlayer grader is weighted marking, and weighting according to object candidate area to the object candidate area obtained in different layers
Divide and be ranked up, the weight score value is higher to show that possibility of the region comprising target is bigger；
(2-4), can be to target candidate in order to reduce a large amount of coverings between candidate region after step (2-3) processing
Region carries out maximum inhibition, obtains final object candidate area.After Fig. 3 show the present invention by Objective algorithm process
What obtained result figure, the wherein left side indicated is original graph, and the right is result figure.Original graph and result figure are compared, it can be found that
Objective algorithm of the present invention can preferably obtain object candidate area, but can also have some false-alarms, it is therefore desirable to target
Candidate region is further processed.
Step 3 trains conspicuousness model.This training stage mainly reaches two purposes：Training random forest returns device, instruction
Practice Multiscale Fusion weight.The partitioning algorithm (Graph-based Segmentation) based on graph model is first passed through, it is right
Original image carries out multi-scale division.Many independent regions can be obtained on each scale, after segmentation, unify to be referred to as herein
For super-pixel block.Random forest returns the significance value that device is used to calculate each super-pixel block after segmentation；Multiscale Fusion is weighed
The obtained Saliency maps being reused under fusion different scale, and obtain final Saliency maps.In embodiments of the present invention, will
MSRA-B is as training set, wherein comprising 5000 figures, each figure has corresponding artificial target's result figure.Step 3 is specific
Including following sub-step：
(3-1) training random forest returns device, using classical Graph-based Segmentation methods, to artwork
N layers of multi-scale division is carried out, in embodiments of the present invention, N takes 15.On each scale, to each obtained after segmentation
Super-pixel block R finds corresponding region H in corresponding handmarking's result figure.If the label of contained pixel has category in the H of region
In foreground/background, then super-pixel block R is labeled as foreground/background, otherwise abandons super-pixel block R, wherein foreground refers to wrapping
Target area containing mesh, background refer to nontarget area.The random forest for learning standard using the training sample of label returns device；
(3-2) training Multiscale Fusion weight, if the multiple dimensioned Saliency maps that each training sample obtains are { S1,
S2..., SN, corresponding handmarking's result is G, and multiple dimensioned linear fusion weight w is trained in a manner of least squaren,
Argmin indicates to take the w when mean square error minimumn, calculation formula is as follows：
Step 4 using trained conspicuousness model in step 3 to original image carry out significance analysis, conspicuousness be from
The visual cognition angle of people is set out, and the vision mode built by physiology, psychology, therefore conspicuousness can be good at instead
The information that attracts people's attention in scene should be gone out.Fig. 4 show the flow chart of detection-phase significance analysis of the present invention, specifically includes following
Sub-step：
(4-1) carries out multiple dimensioned point of N layers using classical Graph-based Segmentation methods, to artwork
It cuts, if obtained segmentation figure is T1, T2..., TN, wherein each tomographic image T after segmentationiAll it is by several independent super-pixel
Block is constituted；
(4-2) is to each tomographic image T after segmentationiIn each super-pixel block, calculate three category features：Area attribute is special
Sign, region contrast feature, region and background contrasts feature.For area attribute feature, the super-pixel block is calculated in difference
The features such as color, texture, histogram in color space (RGB, LAB, HSV)；For region contrast feature, the super picture is calculated
The contrast of plain block and its all of its neighbor block wherein calculates chi-Square measure, non-histogram feature calculation absolute difference between histogram
Value；For region and background contrasts feature, using the peripheral regions of image as background area, according to region contrast feature
Computational methods calculate the contrast of the super-pixel block and background.Finally above-mentioned three classes feature is together in series as the super-pixel block
Feature；
(4-3) extracts corresponding feature according to step (4-2), and returns device using trained random forest in step 3
Recurrence calculating is carried out, each tomographic image T after being dividediIn the corresponding significance value of each super-pixel block, finally may be used
To obtain each tomographic image TiCorresponding Saliency maps Ci；
(4-4) by the trained Multiscale Fusion weight of step 3, the multiple dimensioned Saliency maps { C that will be obtained1, C2...,
CNCarry out linear weighted function obtain final Saliency maps.Fig. 5 show the result figure of detection-phase significance analysis of the present invention,
What wherein the left side indicated is original graph, and the right is result figure.Original graph and result figure are compared, it can be found that conspicuousness can be preferable
The marking area obtained in image.In result figure, brighter place represents that the position conspicuousness is stronger, which is target
Possibility it is bigger, therefore object candidate area can be further analyzed by conspicuousness, obtain the accurate position of target
It sets.
Step 5 is after step 2 processing, so that it may to obtain object candidate area.After step 4 processing, so that it may
To obtain the Saliency maps of target.Contain a large amount of false alarm information in the candidate region obtained due to step 2, so, for every
One candidate region, the Saliency maps that can be obtained by step 4 are further confirmed that, the candidate region of a large amount of false-alarms will be included
It rejects, finally obtains the accurate location of target.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, not to
The limitation present invention, all within the spirits and principles of the present invention made by all any modification, equivalent and improvement etc., should all include
Within protection scope of the present invention.
Claims (6)
1. a kind of waterborne target rapid detection method based on unmanned boat application, which is characterized in that including：
Step 1 trains grader and interlayer grader in layer, wherein grader is for the scale space in structure in the layer
Each layer judges whether current candidate region is target area, and the interlayer grader is used for the weighted calculation between different layers；
Step 2 carries out Objective analysis using grader in the layer and the interlayer grader to original image, obtains final
Object candidate area, including following sub-step：
(2-1) carries out change of scale to the original image, builds pyramid model, obtains the image of different scale size, remembers
For L1,L2,…,LM, wherein M indicates the number of plies of the scale space of structure；
(2-2) is in each tomographic image LiIn, the region of fixed size, meter are extracted to each position using the method for sliding window
The NG features in the region are calculated, and by the score value in the classifier calculated region in the layer, obtain the target candidate of different layers
Region；
(2-3) is weighted marking by the interlayer grader to the object candidate area obtained in different layers, and according to institute
Weighting marking result is stated to be ranked up；
(2-4) carries out maximum inhibition to object candidate area, and obtains the final object candidate area；
Step 3 trains random forest to return device and Multiscale Fusion weight, wherein the random forest returns device for calculating point
Cut the significance value of each rear super-pixel block, the Multiscale Fusion weight be used to merge under different scale obtain it is notable
Property figure；The step 3 includes following sub-step：
(3-1) training random forest returns device：The original image is carried out using the partitioning algorithm based on graph model more
Multi-scale segmentation, to each super-pixel block obtained after segmentation, is found on each scale in corresponding handmarking's result figure
Corresponding region, if the label of contained pixel has and belongs to foreground/background in the corresponding region, by the super-pixel block
Labeled as foreground/background, the super-pixel block is otherwise abandoned, wherein the foreground refers to including mesh target area, the background
Refer to nontarget area, the random forest for learning standard using the training sample of label returns device；
(3-2) training Multiscale Fusion weight wn, calculation formula is as follows：
Wherein, { S1,S2,…,SNIndicate the multiple dimensioned Saliency maps that each training sample obtains；G indicates corresponding handmarking
As a result；Argmin indicates to take the Multiscale Fusion weight when mean square error minimum；
Step 4 returns device and the Multiscale Fusion weight using the random forest and carries out conspicuousness point to the original image
Analysis, obtains final Saliency maps；
Step 5 will include the candidate of a large amount of false-alarms according to the final object candidate area and the final Saliency maps
Region is rejected, and the accurate location of target is finally obtained.
2. the method as described in claim 1, which is characterized in that the step 1 includes following sub-step：
Grader in (1-1) training layer：Target area is directly extracted to training set sample, and is compressed into the block of fixed size
As positive sample, candidate blocks are randomly selected to the training set sample, the candidate blocks are less than with the overlapping of the target area
Fixed threshold is then used as negative sample；
(1-2) training interlayer grader：The adjustment that scale is carried out to the training set sample, obtains the image of different layers,
The block for randomly selecting fixed size, reverts to according to compression factor under original graph, if Chong Die with the target area be more than institute
It states fixed threshold and is then used as positive sample, be otherwise used as negative sample.
3. the method as described in claim 1, which is characterized in that when calculating the NG features in the step (2-2), take all
Horizontal direction maximum of gradients is g in channelx, vertical gradient maximum value is gy, and pass through formula：min(|gx|+|gy|,
255) come calculate each point characteristic value.
4. method as claimed in any one of claims 1-3, which is characterized in that the step 4 includes following sub-step：
(4-1) carries out the original image using the partitioning algorithm based on graph model N layers of multi-scale division, point remembered
It is T to cut figure1,T2,…,TN, wherein each tomographic image T after segmentationiAll it is to be made of several independent super-pixel block；
(4-2) is to each tomographic image T after segmentationiIn each super-pixel block, calculate three category features：Area attribute feature, area
Domain contrast metric, region and background contrasts feature；
(4-3) extracts corresponding feature according to the step (4-2), and returns device using the random forest and carry out recurrence meter
It calculates, each tomographic image T after being dividediIn the corresponding significance value of each super-pixel block, may finally obtain each
Tomographic image TiCorresponding Saliency maps Ci；
(4-4) by the Multiscale Fusion weight, the multiple dimensioned Saliency maps { C that will be obtained1,C2,…,CNLinearly add
Power, obtains the final Saliency maps.
5. method as claimed in claim 4, which is characterized in that in the step (4-2), for the area attribute feature,
Calculate color of the super-pixel block in different colours space, texture, histogram feature；For the region contrast feature,
The contrast of the super-pixel block and its all of its neighbor block is calculated, chi-Square measure, non-histogram feature are wherein calculated between histogram
Calculate absolute difference；For the region and background contrasts feature, using the peripheral regions of image as background area, according to area
The computational methods of domain contrast metric calculate the contrast of the super-pixel block and background.
6. method as claimed in claim 4, which is characterized in that in the step (4-2), which is together in series work
For the feature of the super-pixel block.
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