CN108960069A - A method of the enhancing context for single phase object detector - Google Patents
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Abstract
The present invention relates to a kind of methods of enhancing context for single phase object detector: the training dataset of selected object detection；Construct single phase object detector, main includes two parts: extracting the core network of feature and the sub-network for classifying and detection block returns, core network chooses ResNet50 network, it is improved for classification and the sub-network returned with detection block, design the sub-network that classification sub-network and detector based on enhancing context approach return, classification sub-network and detection block return sub-network and use identical design: constituting a submodule by three different convolution filters of expansion rate, then stack two sub- module composition sub-networks；Using the loss function of multitask；Training.
Description
Technical field
It is the invention belongs to deep learning and computer vision field, in particular to a kind of for single phase object detector
Enhance the method for context.
Background technique
Object detection is the extremely challenging project of computer vision field.Object detection based on deep learning achieves
Significant achievement.Have been widely used for the various fields such as video monitoring, automatic Pilot and human-computer interaction.
The purpose of object detection is to discriminate between object and background, identifies object category, while being accurately located object with detection block
Body.The object detection algorithms of mainstream are divided into two classes: the object detection algorithms of dual-stage and the object detection algorithms of single phase at present.
The object detection algorithms [1] of dual-stage are that object detection problem is divided into two steps: the first step is to suggest that network generates with region
Some candidate regions, second step classify and be adjusted candidate region location to these candidate regions.Single phase object
Physical examination method of determining and calculating [2] is then from image directly positioning object itself.In contrast, dual-stage object detector has higher
The detection speed of detection accuracy, single phase detector is high-efficient fastly.
The difficult point of object detection is that object is blocked with the presence of multiple scales and object, existing object detection algorithms benefit
Issues On Multi-scales are solved with feature pyramid [3-5], are incorporated contextual information [4] and are handled occlusion issue
Bibliography:
[1]Ren S,HeK,GirshickR,etal.FasterR-CNN:towardsreal-time
objectdetectionwith regionproposalnetworks[C]//
InternationalConferenceonNeuralInformationProcessing Systems.MITPress,2015:
91-99.
[2]LiuW,AnguelovD,ErhanD,etal.SSD:Single ShotMultiBoxDetector[J]
.2015:21-37.
[3]Lin TY,DollarP,GirshickR,etal.FeaturePyramidNetworks
forObjectDetection[J].2016:936-944.
[4]CaiZ,FanQ,Feris RS,etal.AUnifiedMulti-
scaleDeepConvolutionalNeuralNetwork forFastObjectDetection[C]//
EuropeanConference onComputerVision.Springer,Cham,2016:354-370.
[5]Lin TY,GoyalP,GirshickR,etal.FocalLoss forDenseObjectDetection[J]
.2017:2999-3007.
[6]Lin,Tsung-Yi,etal."Microsoftcoco:Commonobjectsincontext."European
conference on computervision.Springer,Cham,2014
Summary of the invention
A kind of method of enhancing context for single phase object detector is provided it is an object of that present invention to provide a kind of,
Single phase detector is set to incorporate more contextual informations, it is more efficient for the detection of wisp and the object that is blocked,
It also contributes to reducing false-alarm simultaneously.Technical solution is as follows:
A method of the enhancing context for single phase object detector
1) training dataset of object detection is selected, data set includes picture and mark, marks the position containing detection block
And object category；
2) construct single phase object detector, mainly include two parts: extract feature core network and for classify and
Detection block return sub-network, core network choose ResNet50 network, for classification and with detection block return sub-network into
Row improve, design based on enhancing context approach classification sub-network and detector return sub-network, classification sub-network with
Detection block returns sub-network and uses identical design: constituting a submodule by three different convolution filters of expansion rate, so
Two sub- module composition sub-networks are stacked afterwards；
3) loss function of multitask, the loss function including Classification Loss function and detection block precision, design grid are used
The number and the final condition of convergence of network of network training loop iteration, and initialization network parameter；
4) being input to training data batch in the network is calculated and is trained, the specific steps are as follows:
A) training data is inputted in network, is sequentially inputted to core network and classification sub-network and detection block returns net
Network calculates convolutional layer feature X ∈ RH×W×C, wherein H × W indicates the size of the characteristic pattern of output, and C indicates the characteristic pattern of output
Port number；
B) it calculates and loses and carry out backpropagation, update network weight according to gradient descent method；
C) circulation step a)~b), after successive ignition, loss convergence obtains trained neural network model；
5) when input picture, position and the classification of the middle object of present image can be calculated by the model.
The present invention returns sub-network in the classification sub-network and detection block of single phase detector and introduces parallel expansion rate not
With filter, the receptive field of these filters is of different sizes, to incorporate contextual information abundant, help detect wisp and
There is the object blocked, realize simply, while guaranteeing detection efficiency, effectively promotes the detection performance of single phase detector,
Facilitate wisp and has the object detection blocked.This patent method is applied in RetinaNet [5] network structure, replacement
Classification and detection block originally returns sub-network, object detection experiment is carried out on COCO image data base [6], this patent is to small
The accuracy rate of object detection improves 1 percentage point.
Detailed description of the invention
The network structure of the existing single phase object detector of Fig. 1
The network structure of single phase object detector of the Fig. 2 based on enhancing context
Fig. 3 expands the receptive field of convolution, and the expansion rate r of (a) (b) (c) is respectively 1,2,4
Specific embodiment
This patent is further described with reference to the accompanying drawing.
Fig. 1 describes the network structure of existing single phase object detector.In existing single phase object detector
In network structure, sub-network of classifying and detection block return sub-network design simply, only the simple stacking of n convolutional layer, these
The convolution kernel size of convolutional layer is fixed, and corresponding receptive field is also fixed, available Limited information.Classify sub-network it is defeated
It is the size of characteristic pattern for W × H × KA, W and H out, K is the classification number of object, A and the pre-set default of single phase detector
Box type number is consistent.Detection block return sub-network output be W × H × 4A, W and H be characteristic pattern size, 4 for default frame with
Four offsets of real-world object position, A are characterized the number of types of the default frame of each position of figure.
Fig. 2 describes the network structure of the enhancing context for single phase detector of this patent proposition.Enhancing is up and down
The operation of text is mainly reflected in classification sub-network and detection block returns in sub-network, and sub-network is no longer the convolution of fixed receptive field
The stacking of layer, but the series connection of 2 submodules, detail are as follows:
(1) submodule of integrating context.As shown in Fig. 2, submodule is made of 3 parallel convolution blocks, C1, C2, C3。
The size of parallel convolution kernel is identical, but expansion rate difference (r1=1, r2=2, r3=4).The convolution kernel of different expansion rates is experienced
It is wild different.It is 1 that common convolution, which is considered as expansion rate,.By taking one-dimensional expansion convolution as an example, when input is x, the power of convolution kernel
Weight is w, and the expansion rate of convolution kernel is r, is exported as y, and the expression formula of one-dimensional expansion convolution is as follows:
Wherein, expansion rate is corresponding with the sampling span of input.As seen from Figure 3, the corresponding sense of different expansion rates
It is of different sizes by open country, it is equally 3 × 3 convolution kernel, as r=2, receptive field is the 2.5 of r=12Times, the impression as r=4
5 when open country is r=12Times.The big receptive field of expansion rate is big, can incorporate more contextual informations.
(2) Fusion Features.The output of each parallel filter of submodule has different receptive fields, the big filtering of expansion rate
The contextual information that the output of device obtains is abundanter, and the output characteristic pattern size of each parallel branch is identical, is added point by point, then
It inputs in a common convolution filter together, i.e. C4 in Fig. 2, carries out Fusion Features, the feature extracted is made to have more robust
Property.
(3) parameter sharing.Existing single phase detector [2] [5] can be to difference point in order to detect multiple dimensioned object
The feature output of resolution carries out multistage detection output, then the filter of the corresponding configuration of multistage classification sub-network can be with parameter
It is shared, parameter amount is reduced, similarly, detection blocks at different levels return sub-network can also be with parameter sharing.Thus in enhancing proposed in this paper
The ginseng negligible amounts that network structure hereafter introduces, the influence to detection rates are little.
Technical solution of the present invention will be clearly and completely described below, it will be to single phase detector in description
The method that RetinaNet [5] incorporates enhancing contextual information, it is clear that described embodiment is only that a part of the invention is real
Example, rather than whole examples.
Apply the present invention in object detection task, mainly include three steps: preparing data set；It designs and trains base
In the single phase detector of enhancing context approach；Test/apply detection model.It implements step and is described as follows:
Step 1: preparing data set.
(1) suitable object detection data set is selected.The data set of current more common object detection has PascalVOC
With COCO etc., there is the label information of object category and object detection frame.Image size in data set is not fixed, can in training
Image one side size is arranged and fixes, another side limits maximum length according to the demand of actual hardware condition and application.As
A kind of example, we use COCO data set [6], and in the format that this data uses for picture short side a length of 800, long side is most very much not
Color image format more than 1333x3, all images are by the data enhancing overturn at random and normalization operation.
(2) image set divides.COCO2014 data set includes training set, verifying collection and test set.We will use training set
With the verifying collection single phase detector that training is enhanced based on context together, test set is follow-up test modelling effect or reality
Using when use.
Step 2: designing and training the single phase detector based on context Enhancement Method.
Design the single phase detector based on context Enhancement Method.It is special that image is suitably extracted in entire design including selection
The core network of sign, and classify, the number of the submodule for the integrating context information that detection block sub-network includes, submodule institute
The number of parallel filter, the size and expansion rate of convolution kernel design the number of plies of convolutional layer, and melt for feature
The convolution filter structure of conjunction, the number and the final condition of convergence of network of planned network training loop iteration, and initialize network
Parameter.Using ResNet50 as core network in the present invention, while the thought of feature pyramid network [3] is also applied, adopted
With from top to bottom with the structure of horizontal-associate come reinforce different resolution characteristic pattern semanteme, the design of sub-network selects 2 submodules
Block series connection, there are three parallel convolution filters respectively for each submodule, and wherein their convolution kernel size is all 3 × 3, expansion
Rate is respectively 1,2 and 4, and the output channel number of convolution filter is all 256.To reduce computation complexity, using 1 × 1 convolution
Filter is as Fusion Features filter.Since COCO data set has 80 type objects, so K=81.Setting in same [5], I
Set 9 for A, the combination of corresponding three kinds of different areas and Aspect Ratio.The area from 32 to 512 of default frame is evenly distributed to
The outputs at different levels of feature pyramid network, the length-width ratio for defaulting frame have 1/2,1,2.
(1) the good single phase detector based on context Enhancement Method of first initialization design, core network are
ResNet50 its weights initialisation in ImageNet data set classification based training, remaining network layer random initializtion.
(2) the single phase detector based on context Enhancement Method is then trained, training image batch is input to this
In network, is calculated and is trained, the specific steps are as follows:
A) training image data are inputted in core network, extracts the feature of picture.
B) feature of calculating is passed into corresponding classification sub-network again and detection block returns sub-network.
1. characteristic pattern successively passes through two submodules of sub-network.Parallel convolution kernel expansion rate is different in submodule, right
Receptive field of different sizes is answered, contextual information more abundant can be obtained, then, the output feature of parallel-convolution filter
It is added, is input in the filter of Fusion Features pixel-by-pixel, using filter ω ∈ R1×1×256×256.Mixing operation introduces few
Parameter to be learned is measured, computation complexity is reduced.
It is lost 2. the output for sub-network of classifying and true value are calculated using focal loss function, will test frame Recurrent networks
Output and true value using Smooth L1 function calculate lose.
C) circulation step a)~b), after successive ignition, loss function convergence obtains trained neural network mould
Type.
Step 3: the trained network model of test/reference.
(1) test set data are got out, call designed network structure and trained network parameter, and by test chart
Piece batch or individual be input in trained model.
(2) image data is passed sequentially through core network by forward calculation, and sub-network of classifying and detection block return sub-network.
Classification sub-network output is that detection block belongs to all kinds of probability, and class of the maximum classification of select probability as final detection block
Not, what detection block returned sub-network output is the offset of opposite default frame, obtains more accurate detection frame knot by offset
Fruit.After non-maxima suppression, class probability is regarded as into final testing result greater than given threshold.
Claims (1)
1. a kind of method of the enhancing context for single phase object detector, including the following steps:
1) training dataset of object detection is selected, data set includes picture and mark, marks the position containing detection block and object
Body classification；
2) single phase object detector is constructed, mainly includes two parts: extracting the core network of feature and for classifying and detecting
The sub-network that frame returns, core network choose ResNet50 network, are changed for classification and the sub-network returned with detection block
Into designing the sub-network that classification sub-network and detector based on enhancing context approach return, classification sub-network and detection
Frame returns sub-network and uses identical design: constituting a submodule by three different convolution filters of expansion rate, then heap
Fold two sub- module composition sub-networks；
3) loss function of multitask, the loss function including Classification Loss function and detection block precision, planned network instruction are used
Practice the number and the final condition of convergence of network of loop iteration, and initialization network parameter；
4) being input to training data batch in the network is calculated and is trained, the specific steps are as follows:
A) training data is inputted in network, is sequentially inputted to core network and classification sub-network and detection block Recurrent networks,
Calculate convolutional layer feature X ∈ RH×W×C, wherein H × W indicates the size of the characteristic pattern of output, and C indicates the channel of the characteristic pattern of output
Number；
B) it calculates and loses and carry out backpropagation, update network weight according to gradient descent method；
C) circulation step a)~b), after successive ignition, loss convergence obtains trained neural network model；
5) when input picture, position and the classification of the middle object of present image can be calculated by the model.
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Application publication date: 20181207 |