CN110349148A - A kind of image object detection method based on Weakly supervised study - Google Patents
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Abstract
The invention discloses a kind of image object detection methods based on Weakly supervised study, belong to technical field of machine vision.Image data set is collected first, using the depth convolutional neural networks model based on Analysis On Multi-scale Features figure of multi-instance learning method training building；Then real image is inputted, the classification thermodynamic chart of depth convolutional neural networks model extraction real image is passed through；Finally using the encirclement frame of target in binary image connected component analysis method output classification thermodynamic chart, object detection results are obtained.The present invention, which is used, realizes image object Detection task based on Weakly supervised learning method, image level classification annotation information, which is used only, in mark in convolutional neural networks model training can be completed object detection task, target needed for being different from the prior art surrounds frame markup information, the work of target in artificial mark image is greatly reduced, so that completing image object Detection task has more economic benefit.
Description
Technical field
The invention belongs to nerual network technique fields, and in particular to a kind of image object detection side based on Weakly supervised study
Method.
Background technique
With deep learning in Computer Vision Task using more and more extensive, instantly deep neural network model is being schemed
As being excellent in the tasks such as classification, target detection, semantic segmentation, but supervised learning formula training pattern need it is largely artificial
Markup information, the relevant mark of these markup informations, especially position, often expends a large amount of manpower and material resources, therefore to mark
Note information, which relies on lower Weakly supervised learning method, becomes research hotspot.Weakly supervised study is a kind of mode of machine learning,
It is different from supervised learning model needs mark to correspond with model output, the markup information that Weakly supervised study relies on only needs portion
Hierarchical mark, therefore Weakly supervised study has a good application prospect in actual computer visual task and economic benefit.
(1) image characteristics extraction
Traditional characteristic extracts the means for mainly using image procossing, hand-designed feature mode and corresponding extraction side
A large amount of artificial trace has been incorporated during method, design and extraction, while labor intensive, has also been unfavorable for from data itself
Angle carry out information excavating.The method of convolutional neural networks then utilizes network to be capable of the characteristic of self study, realizes from design
Artificial excessively intervention is avoided in journey as best one can, while the process of simplification, moreover it is possible to reach recognition effect more better than conventional method,
And the multilayered structure of deep neural network can learn to higher, more abstract expression, using multi-scale feature fusion
Mode can extract the more exact feature of image.
(2) target detection
Object detection task is common a kind of Computer Vision Task in production and living, it is required that model exports target
Encirclement frame target category corresponding with the frame, in subsequent task.The common target based on depth convolutional network
Detection model all uses the encirclement frame manually marked in training as supervision message, usually using neural net regression target
The thinking of frame coordinate is completed.Object detection method based on Weakly supervised study does not depend on the encirclement frame information manually marked, greatly
Reduce the manpower and material resources cost in mark work.
Summary of the invention
It is an object of the invention to: to solve the existing image object detection method based on deep learning for manually marking
The technical issues of depending on unduly, design depth convolutional neural networks model extraction based on Analysis On Multi-scale Features figure and generate image
Classification thermodynamic chart, the classification thermodynamic chart based on model output realize image object Detection task, propose a kind of based on Weakly supervised
The image object detection method of habit.
The technical solution adopted by the invention is as follows:
A kind of image object detection method based on Weakly supervised study, this method comprises the following steps:
Step 1: image data set is collected, using the depth based on Analysis On Multi-scale Features figure of multi-instance learning method training building
Spend convolutional neural networks model；
Step 2: input real image passes through the classification thermodynamic chart of depth convolutional neural networks model extraction real image；
Step 3: using the encirclement frame of target in binary image connected component analysis method output classification thermodynamic chart, obtaining
Object detection results.
Preferably, the step 1 includes the following steps:
Step 1.1: compiling image data, normalized is done to picture size, and more heat coding marks are made to image
Note；
Step 1.2: depth convolutional neural networks model of the building based on Analysis On Multi-scale Features figure, depth convolutional neural networks mould
The core network model of type uses the model through public data collection pre-training；
Step 1.3: the depth convolutional neural networks model of building is trained using multi-instance learning method, wherein labeled data
Use more heat coding marks in step 1.1.
Preferably, step 2.2 includes the following steps:
Step 2.2.1: choosing the different down-sampling stages in the core network model of depth convolutional neural networks model, right
The real image of input extracts Analysis On Multi-scale Features by feature extraction network；
Step 2.2.2: Analysis On Multi-scale Features carry out global multiple dimensioned pond after convolutional layer is converted, and export multi-class point
Class probability value.
Preferably, the step 1.3 includes the following steps:
Step 1.3.1: predicted value is exported as multi-class by more heat coding marks and depth convolutional neural networks model
Probability, to calculate cross entropy loss function；Using bloom loss function to the output feature in depth convolutional neural networks model
Figure is constrained, and forces the response on the output characteristic pattern in convolutional network model to be drawn close toward certainty eminence, and this is handed over
Pitch the overall loss function of the sum of entropy loss function and bloom loss function as model；
Step 1.3.2: gradient decline optimization, training depth convolution are carried out using optimizer to total losses function in training
Neural network model is until convergence, i.e., overall loss function fluctuating range is maintained within 0.1 in 5 wheel training.
Preferably, the step 2 includes the following steps:
Step 2.1: dimension normalization processing is carried out for the real image of input；
Step 2.2: the real image through normalized is obtained into each spy by depth convolutional neural networks model treatment
Levy the classification thermodynamic chart of scale；
Step 2.3: the classification thermodynamic chart after width Fusion Features that the classification thermodynamic chart of each characteristic dimension is permeated.
Preferably, the step 2.2 includes the following steps:
Step 2.2.1: choosing the different down-sampling stages in the core network model of depth convolutional neural networks model, right
The real image of input extracts Analysis On Multi-scale Features by feature extraction network；
Step 2.2.2: Analysis On Multi-scale Features carry out global multiple dimensioned pond after convolutional layer is converted, and export multi-class point
Class probability value.
Preferably, the step 2.3 includes the following steps:
Step 2.3.1: the corresponding classification thermodynamic chart of all characteristic dimensions is sampled to the practical figure after normalized
The size of picture；
Step 2.3.2: in each position, the response to the corresponding classification thermodynamic chart of multiple characteristic dimensions is averaged, and is obtained
Classification thermodynamic chart after Fusion Features.
Preferably, specific step is as follows in the step 3:
Step 3.1: using the classification results that depth convolutional neural networks model exports as the classification foundation of target, selecting class
There are the corresponding characteristic patterns of class in other thermodynamic chart；Wherein, output category probability value is thought more than or equal to 0.5 with the presence of such,
Otherwise it is assumed that such is not present；
Step 3.2: the response mean value in the corresponding characteristic pattern of each class is calculated, using response mean value as binaryzation threshold
Value, and by the corresponding characteristic pattern binaryzation of each class；
Step 3.3: using connected component analysis method, take 8- domain pattern, the characteristic pattern after binaryzation is connected
Logical regional analysis marks target area of each region as corresponding class；
Step 3.4: surrounding frame for the minimum circumscribed rectangle of the target area of each class as corresponding classification target；
Step 3.5: will be all there are the corresponding encirclement frame output of classification in classification thermodynamic chart, complete image object detection.
In conclusion by adopting the above-described technical solution, the beneficial effect that the present invention is different from the prior art is:
1, the present invention is based on Weakly supervised learning method using one kind, and method is mainly by using multi-instance learning
Method trains depth convolutional neural networks model, then real image is input to the depth convolutional neural networks model trained
In, the classification thermodynamic chart of real image is extracted, classification thermodynamic chart is finally exported using binary image connected component analysis method
The encirclement frame of middle target, can be obtained object detection results.The present invention, which is used, realizes image mesh based on Weakly supervised learning method
Detection task is marked, image level classification annotation information, which is used only, in the mark in convolutional neural networks model training can be completed target
Detection task, target needed for being different from the prior art surround frame markup information, greatly reduce target in artificial mark image
Work so that complete image object Detection task have more economic benefit.
It 2,, can be from different rulers by constructing the depth convolutional neural networks model based on Analysis On Multi-scale Features in the present invention
It spends scope and extracts feature, the classification thermodynamic chart of generation can more react the response region of target, be different from existing convolutional network
The single disadvantage of feature extraction scale caused by model structure, the present invention in depth convolutional neural networks model structure guarantee
The accuracy of target detection.
3, in the present invention, propose that the new loss function of one kind is introduced in training convolutional network model is referred to as high light loss
Function, it acts as the output characteristic patterns in constraint convolutional network model, and then the classification thermodynamic chart for generating model finally is more
Stick on conjunction target zone, compared to do not use the loss function model, further increase target detection accuracy and effectively
Property.
Detailed description of the invention
Fig. 1 is that the present invention is based on the flow diagrams of the image object detection method of Weakly supervised study；
Fig. 2 is the block schematic illustration of the image object detection method based on Weakly supervised study in the present invention；
Fig. 3 is depth convolutional neural networks model schematic in the present invention.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, not
For limiting the present invention.
Present embodiment is detected for a secondary real scene image, and the training of model uses public data collection VOC
Pascal is carried out, and pre-training core network module is using the sorter network trained on public data collection ImageNet
Inception-ResNet-v2 is carried out, and target category (classification results) is aircraft, bicycle, birds, ship, bottle, public transport
Vehicle, car, cat class, chair, ox class, dining table, dog class, horse class, motorcycle, the mankind, potting, sheep class, sofa, train and display
Device etc..
A kind of image object detection method based on Weakly supervised study, this method comprises the following steps:
Step 1: VOC Pascal data set is used, using image data set and the more classification annotations of correspondence image grade as defeated
Enter, more example training are carried out to the depth convolutional neural networks model based on Analysis On Multi-scale Features figure.Specific step is as follows:
Step 1.1: the picture size concentrated to data does normalized, adopts size normalization operation unanimously for image
Sampling obtains more heat coding marks of the corresponding classification of image to 513 × 513, using the classification information in data set；
Step 1.2: depth convolutional neural networks model of the building based on Analysis On Multi-scale Features figure, core network use
Inception-ResNet-v2 model removes the part of classification layer；
Step 1.3: using the depth convolutional neural networks model of the method training building of multi-instance learning.Including following step
It is rapid:
Step 1.3.1: predicted value is exported as multi-class by more heat coding marks and depth convolutional neural networks model
Probability, to calculate cross entropy loss function, formula such as: L=-ylog (y ')-(1-y) log (1-y '), wherein y is model output
Multi-class probability, y ' be image level classification mark；Using bloom loss function to defeated in depth convolutional neural networks model
Characteristic pattern is constrained out, forces the response on the output characteristic pattern in convolutional network model to be drawn close toward certainty eminence, i.e.,
Toward 1 or 0 approach, specifically in the bloom loss function calculation formula of certain position scale feature figure (i, j) are as follows: H (i, j)=- p (i,
J) logp (i, j)-(1-p (i, j)) log (1-p (i, j)), wherein p indicates the response on this feature figure, and i indicates abscissa, j
Indicate ordinate, and the overall loss function by the sum of this cross entropy loss function and bloom loss function as model；
Step 1.3.2: total losses function is carried out under gradient using optimizer (being not limited to Adam optimizer) in training
Drop optimization, training pattern carry out gradient decline optimization, are divided into two stage-trainings, and the first stage is finely adjusted model, and second
Stage-training overall model, training depth convolutional neural networks model is until convergence, i.e., the overall loss function wave in 5 wheel training
Dynamic amplitude is maintained within 0.1.
Step 2: input real image passes through the model extraction trained and merges its classification thermodynamic chart.Specific steps are such as
Under:
Step 2.1: scale being carried out for the real image of input and is sampled to 513 × 513 (normalizeds)；
Step 2.2: processed real image is obtained into the classification thermodynamic chart of each characteristic dimension by model；Depth convolution
The classification layer part for giving up core network in neural network model obtains the defeated of different characteristic scale by the down-sampling stage three times
Out and design conversion layer；The following steps are included:
Step 2.2.1: the different down-sampling stages in the core network model of pre-training are chosen, to the real image of input
Analysis On Multi-scale Features (features of i.e. multiple semantic scales) are extracted by feature extraction network；
Step 2.2.2: Analysis On Multi-scale Features carry out global multiple dimensioned pond, specifically, by each scale after convolutional layer is converted
Under corresponding characteristic pattern convert to port number as the characteristic pattern of M × C by one layer of convolutional layer, wherein C is target classification classification number,
Taking 20, M in this example is different characteristic parameter in every one kind, and M often takes 10 in an implementation, hereafter to each M layers in channel dimension
Average pond is carried out, the classification thermodynamic chart that port number is C is obtained, reuses the global pond top-k, that is, take and taken on classification thermodynamic chart
Maximum output of the k value as this layer, then k takes 20 in implementing, finally by output corresponding under each scale summation and general
The multi-class class probability value of rate final output.
Step 2.3: the classification thermodynamic chart after width Fusion Features that the classification thermodynamic chart of each characteristic dimension is permeated.Including
Following steps:
Step 2.3.1: by the corresponding classification thermodynamic chart use of all characteristic dimensions be resampled to input image size 513 ×
513；
Step 2.3.2: in each position of characteristic pattern that port number is C to the corresponding classification thermodynamic chart of multiple characteristic dimensions
Value is averaged, the classification thermodynamic chart after obtaining Fusion Features.
Step 3: classification thermodynamic chart and class probability based on the output of depth convolutional neural networks model, using binary picture
As the encirclement frame of target in connected component analysis method output classification thermodynamic chart, object detection results are obtained.Specific step is as follows:
Step 3.1: using the classification results that depth convolutional neural networks model exports as the classification foundation of target, selecting class
There are the corresponding characteristic patterns of class in other thermodynamic chart；Wherein, output category probability value is thought more than or equal to 0.5 with the presence of such,
Otherwise it is assumed that such is not present；
Step 3.2: the response mean value in the corresponding characteristic pattern of each class is calculated, using response mean value as binaryzation threshold
Value, by the corresponding characteristic pattern binaryzation of each class, specifically, the position that characteristic pattern response is more than or equal to binarization threshold is set as
1, otherwise it is set as 0；
Step 3.3: using the connected component analysis method in image procossing, 8- domain pattern being taken (to regard a point week
Enclosing 8 direction points is all field), connected component analysis is carried out to the characteristic pattern after binaryzation, marks each region as corresponding class
Target area；
Step 3.4: using the minimum circumscribed rectangle through each target area marked in the corresponding characteristic pattern of each class as
Corresponding classification target surrounds frame；
Step 3.5: surrounding frame by all in classification thermodynamic chart there are the corresponding target of classification and exported together with classification, complete figure
As object detection task.
Claims (7)
1. a kind of image object detection method based on Weakly supervised study, which comprises the steps of:
Step 1: collecting image data set, rolled up using the depth based on Analysis On Multi-scale Features figure of multi-instance learning method training building
Product neural network model；
Step 2: input real image passes through the classification thermodynamic chart of depth convolutional neural networks model extraction real image；
Step 3: using the encirclement frame of target in binary image connected component analysis method output classification thermodynamic chart, obtaining target
Testing result.
2. a kind of image object detection method based on Weakly supervised study according to claim 1, which is characterized in that described
Step 1 includes the following steps:
Step 1.1: compiling image data, normalized is done to picture size, and more heat coding marks are made to image；
Step 1.2: depth convolutional neural networks model of the building based on Analysis On Multi-scale Features figure, depth convolutional neural networks model
Core network model uses the model through public data collection pre-training；
Step 1.3: using the depth convolutional neural networks model of multi-instance learning method training building, wherein labeled data is used
More heat coding marks in step 1.1.
3. a kind of image object detection method based on Weakly supervised study according to claim 2, which is characterized in that described
Step 1.3 includes the following steps:
Step 1.3.1: exporting predicted value as multi-class probability by more heat coding marks and depth convolutional neural networks model,
To calculate cross entropy loss function；The output characteristic pattern in depth convolutional neural networks model is carried out using bloom loss function
Constraint, forces the response on the output characteristic pattern in convolutional network model to be drawn close toward certainty eminence, and this cross entropy is damaged
Lose the overall loss function of the sum of function and bloom loss function as model；
Step 1.3.2: gradient decline optimization, training depth convolutional Neural are carried out using optimizer to total losses function in training
Network model is until convergence.
4. a kind of image object detection method based on Weakly supervised study according to claim 2, it is characterised in that: described
Step 2 includes the following steps:
Step 2.1: dimension normalization processing is carried out for the real image of input；
Step 2.2: the real image through normalized is obtained into each feature ruler by depth convolutional neural networks model treatment
The classification thermodynamic chart of degree；
Step 2.3: the classification thermodynamic chart after width Fusion Features that the classification thermodynamic chart of each characteristic dimension is permeated.
5. a kind of image object detection method based on Weakly supervised study according to claim 4, which is characterized in that described
Step 2.2 includes the following steps:
Step 2.2.1: the different down-sampling stages in the core network model of depth convolutional neural networks model are chosen, to input
Real image by feature extraction network extract Analysis On Multi-scale Features；
Step 2.2.2: Analysis On Multi-scale Features carry out global multiple dimensioned pond, it is general to export multi-class classification after convolutional layer is converted
Rate value.
6. a kind of image object detection method based on Weakly supervised study according to claim 4, it is characterised in that: described
Step 2.3 includes the following steps:
Step 2.3.1: the corresponding classification thermodynamic chart of all characteristic dimensions is sampled to the real image after normalized
Size；
Step 2.3.2: in each position, the response to the corresponding classification thermodynamic chart of multiple characteristic dimensions is averaged, and obtains feature
Fused classification thermodynamic chart.
7. a kind of image object detection method based on Weakly supervised study according to claim 5, it is characterised in that: described
Step 3 includes the following steps:
Step 3.1: using the classification results that depth convolutional neural networks model exports as the classification foundation of target, selecting classification heat
There are the corresponding characteristic patterns of class in trying hard to；Wherein, with the presence of such, otherwise output category probability value is thought more than or equal to 0.5
Think that such is not present；
Step 3.2: the response mean value in the corresponding characteristic pattern of each class is calculated, using response mean value as binarization threshold,
And by the corresponding characteristic pattern binaryzation of each class；
Step 3.3: using connected component analysis method, take 8- domain pattern, connected region is carried out to the characteristic pattern after binaryzation
Domain analysis marks target area of each region as corresponding class；
Step 3.4: surrounding frame for the minimum circumscribed rectangle of the target area of each class as corresponding classification target；
Step 3.5: will be all there are the corresponding encirclement frame output of classification in classification thermodynamic chart, complete image object detection.
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