CN108038502A - Object collaborative detection method based on convolutional neural networks - Google Patents
Object collaborative detection method based on convolutional neural networks Download PDFInfo
- Publication number
- CN108038502A CN108038502A CN201711295915.XA CN201711295915A CN108038502A CN 108038502 A CN108038502 A CN 108038502A CN 201711295915 A CN201711295915 A CN 201711295915A CN 108038502 A CN108038502 A CN 108038502A
- Authority
- CN
- China
- Prior art keywords
- group
- fraction
- candidate
- picture
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computational Linguistics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Image Analysis (AREA)
Abstract
The present invention provides a kind of object collaborative detection method based on convolutional neural networks, comprises the following steps:Step 1:On one group of associated picture of content, using existing candidate's generation method, the candidate prediction result and objectivity fraction of every width picture in this group of image are obtained;Step 2:The candidate prediction result obtained based on step 1 obtains the repeated matrix of the group picture piece;Step 3:With reference to objectivity fraction and the repeated matrix of the group picture piece, the prediction result of the final consideration object repeatability of this group of image is obtained.The repeatability of object collaborative detection method combination single image target prediction and multiple image content proposed by the present invention based on convolutional neural networks, more relationship informations are added for detection prediction result, enhance the specific aim of traditional detection and the detection result to selecting classification target.
Description
Technical field
The present invention relates to technical field of image processing, more particularly to a kind of object cooperation detection based on convolutional neural networks
Method.
Background technology
With the fast development of computer vision technique, digital image data increases substantially, and how effectively to handle this
A little data and therefrom acquisition key message are receive much concern the problem of.Target detection is image procossing and computer vision
One important branch, in the field extensive use such as intelligent monitor system, industrial detection, intelligent transportation system, aerospace.Image
Be people obtain external information an important sources, and most of information of image come from it includes destination object, to figure
The target detection of piece is one of computer vision field task the most basic.Target detection technique, is exactly detected in picture
And the target of certain setting species is positioned, so as to obtain the key message of image, this is very important one in image processing process
Step.Therefore the effect of target detection is further improved, is of great significance for obtaining more accurate image information tool.
Had been widely used at present in computer vision application field, deep neural network, it passes through feature extraction
With Feature Mapping process, it can preferably learn the invariant feature into image, can adaptively construction feature be retouched under training
State, there is higher flexibility and universality.Wherein convolutional neural networks are the god with reference to artificial neural network and Deep Learning
It is special through network system, the overall situation training combined with local sensing region, hierarchically structured, feature extraction and assorting process etc.
Point, is widely used in target detection, image segmentation, conspicuousness detection etc., to improve the processing for view data.
In recent years, domestic and international association area had carried out the target detection based on deep neural network to study and obtain for many years
Many achievements.With the rapid development of multimedia technology, it is gradual that similar or consistent target is found from multiple image
As a kind of new demand.Target detection is carried out using deep neural network, existing method is roughly divided into two classes, first, being based on
RCNN methods, its main flow are:The extraction of candidate frame is carried out using gathering or sliding window formula method on the basis of artwork, is obtained
To a series of candidate frame;The feature of image candidate is extracted using convolutional neural networks, then is classified based on these features
Prediction, obtains multiple prediction results and carries out recurrence amendment.Another kind is the method directly predicted based on picture, such as YOLO pin
To entire image, 98 prediction blocks and each prediction fraction are drawn from the angle of recurrence.These researchs are based on single at present
The method of width image, the information that so only only used single image is not comprehensive.It is mutually related image for one group of content,
Cooperation detection is mutually aided in by the association between considering multiple image, similar target is found from multiple image, effectively
Using one group of associated advantage of picture material, at the same can to the uncorrelated factor such as the non-targeted object of each image or background into
Row suppresses.Therefore, it is worthy of expecting for the cooperation detection effect of multiple image for compared to single image.
The content of the invention
It is an object of the invention to improve the problems of the above-mentioned prior art, propose that a kind of content that is based on is associated picture
The method of the object cooperation detection of group and convolutional neural networks, to add several figures in the detection method based on single picture originally
The information of piece repeatability, for improving the specific aim and accuracy of detection.We conducted substantial amounts of experiment, it was demonstrated that the detection side
Method can combine the prediction result of single image and the repeatability between several, so that improving content is associated the inspection of picture group
Effect is surveyed, there is good applicability.
A kind of object collaborative detection method based on convolutional neural networks, comprises the following steps:
Step 1:On one group of associated picture of content, using existing candidate's generation method, obtain every in this group of image
The candidate prediction result and objectivity fraction of width picture;
Step 2:The candidate prediction result obtained based on step 1 obtains the repeated matrix of the group picture piece;
Step 3:With reference to objectivity fraction and the repeated matrix of the group picture piece, the final consideration pair of this group of image is obtained
As the prediction result of repeatability.
Further, method as described above, the step 2 include:
Step 1:All candidate's picture blocks of a group picture piece obtained for step 1, are obtained often based on similarity measurement network
Similitude is matched as a result, obtaining the affinity score of each two between two candidate predictions;
Step 2:The repeated matrix of the group picture piece candidate prediction is generated according to similarity scores.
Further, method as described above, the step 3 include:
Step a:The repeated matrix obtained for step 2, repeated fraction is used as using its maximal eigenvector;
Step b:The objectivity fraction that will be predicted in step 1, and the repeated fraction combination in step a, obtain final
Prediction result fraction.
Beneficial effect:
Object collaborative detection method combination single image target prediction proposed by the present invention based on convolutional neural networks and
The repeatability of multiple image content, adds more relationship informations for detection prediction result, enhances the pin of traditional detection
Detection result to property and to selected classification target, on the basis of single image detection, with reference to the content between image in group
Contact so that network uses the relevance of content between image, so as to improve detection network for one group of association picture
Detection result.
Brief description of the drawings
Fig. 1 is the object collaborative detection method flow chart of the invention based on convolutional neural networks.
Fig. 2 is the practical operation flow chart of the object collaborative detection method of the invention based on convolutional neural networks.
Embodiment
To make the object, technical solutions and advantages of the present invention clearer, below technical solution in the present invention carry out it is clear
Chu, be fully described by, it is clear that described embodiment is part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other without creative efforts
Embodiment, belongs to the scope of protection of the invention.
Fig. 1 is the object collaborative detection method flow chart of the invention based on convolutional neural networks, and Fig. 2 is the present invention based on volume
The practical operation flow chart of the object collaborative detection method of product neutral net, with reference to Fig. 1 and its Fig. 2, method provided by the invention
Comprise the following steps:
Step 1: on one group of associated picture of content, using existing candidate's generation method, obtain every in this group of image
The candidate prediction result and objectivity fraction of width picture.
Specifically, which includes following 3 steps:
1. being associated the picture set A of picture firstly, for one group of n width content, extracted to obtain each width with YOLO methods
Picture aiThe predicting candidate frame of (i=1,2 ..., n).
2. assuming that every width picture generates m candidate prediction, then n × m candidate of this group of n width picture can be obtained altogether
FrameAs candidate prediction as a result, wherein i is picture label, p is a width picture
Candidate frame label.
3. meanwhile obtain the possibility fraction that each candidate frame includes target That is the objectivity fraction of i-th p-th of candidate frame of width figure.
Step 2: repeatability point is obtained using patch match similarity measurement networks based on the candidate prediction result
Matrix number.
Patch match similarity measurement network operating principles are as follows:Selection needs matched two segments, first by its ruler
It is very little to normalize to 64 × 64, gray level image is then converted to, a twin-channel image is finally merged into and is used as input, output
As a result it is final defeated with it for the matching fraction of two picture blocks of input, the matching degree of two picture blocks, that is, similarity degree
It is directly proportional to go out fraction.
1. on the basis of step 1 obtains n × m candidate prediction, by the every m candidate frame of width picture of n width pictures,Matching fraction is respectively obtained between each two, willWith Inputted two-by-two as picture block
In patch match similarity measurement networks, each two obtains a similarity scores k in n × m candidatei,j。
2. the similarity scores k between n × m picture blocki,jForm the repeated matrix of this group of image, matrix
Scale is (n × m) × (n × m).Wherein i-th of candidate of digital representation of the i-th row jth row of matrix and j-th candidates match
Similarity scores ki,j。
Step 3: draw final prediction result with reference to two fractions.
1. after step 2 obtains the repeated matrix of this group of pictures A, be calculated the maximum feature of the matrix to
Amount, the maximal eigenvector of matrix represent feature the most obvious in this matrix, after normalized, with this n × m tie up to
Measure conduct1,2 ..., m) repeated fraction Corresponding is in this group picture pieceThe repeated fraction of candidate blocks, represents in this group of n width picture, the candidate frame
The multiplicity that the similar candidate frame of content occurs.The number that the content that fraction is higher to be represented in the candidate occurs is more, be exactly
Multiplicity is higher in different pictures.
2. it is last, with reference in step 1In previous step By this
Two fractions are calculated according to the following formula, and comprehensive objectivity fraction and repeated fraction, obtain final prediction fraction Wherein λ is balance pair
As property fraction and the constant parameter of repeated fraction, general 0.8≤λ≤1.0.The scoring is higher, illustrates institute in corresponding candidate frame
Comprising object be in pictures A share target probability it is bigger.Thus can be by combining other phases in a group picture piece
The information of picture is closed, to achieve the purpose that the candidate's sequence for correcting every width picture, so as to more optimize the quality level of candidate.
Finally it should be noted that:The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations;Although
The present invention is described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that:It still may be used
To modify to the technical solution described in foregoing embodiments, or equivalent substitution is carried out to which part technical characteristic;
And these modification or replace, do not make appropriate technical solution essence depart from various embodiments of the present invention technical solution spirit and
Scope.
Claims (3)
1. a kind of object collaborative detection method based on convolutional neural networks, it is characterised in that with reference to several content associated images
Relevant information, comprise the following steps:
Step 1:On one group of associated picture of content, using existing candidate's generation method, every width figure in this group of image is obtained
The candidate prediction result and objectivity fraction of piece;
Step 2:The candidate prediction result obtained based on step 1 obtains the repeated matrix of the group picture piece;
Step 3:With reference to objectivity fraction and the repeated matrix of the group picture piece, the final consideration object weight of this group of image is obtained
The prediction result of renaturation.
2. according to the method described in claim 1, it is characterized in that, the step 2 includes:
Step 1:All candidate's picture blocks of a group picture piece obtained for step 1, each two is obtained based on similarity measurement network
Similitude is matched as a result, obtaining the similarity scores of each two between candidate prediction;
Step 2:The repeated matrix of the group picture piece candidate prediction is generated according to similarity scores.
3. according to the method described in claim 2, it is characterized in that, the step 3 includes:
Step a:The repeated matrix obtained for step 2, the repeatability of the group picture piece point is used as using its maximal eigenvector
Number;
Step b:The objectivity fraction that will be predicted in step 1, and the repeated fraction combination in step a, obtain final prediction
Fraction.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711295915.XA CN108038502A (en) | 2017-12-08 | 2017-12-08 | Object collaborative detection method based on convolutional neural networks |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711295915.XA CN108038502A (en) | 2017-12-08 | 2017-12-08 | Object collaborative detection method based on convolutional neural networks |
Publications (1)
Publication Number | Publication Date |
---|---|
CN108038502A true CN108038502A (en) | 2018-05-15 |
Family
ID=62101574
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201711295915.XA Pending CN108038502A (en) | 2017-12-08 | 2017-12-08 | Object collaborative detection method based on convolutional neural networks |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN108038502A (en) |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111046917A (en) * | 2019-11-20 | 2020-04-21 | 南京理工大学 | Object-based enhanced target detection method based on deep neural network |
Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102982539A (en) * | 2012-11-09 | 2013-03-20 | 电子科技大学 | Characteristic self-adaption image common segmentation method based on image complexity |
CN103268607A (en) * | 2013-05-15 | 2013-08-28 | 电子科技大学 | Common object detection method on weak supervision condition |
US8885887B1 (en) * | 2012-01-23 | 2014-11-11 | Hrl Laboratories, Llc | System for object detection and recognition in videos using stabilization |
CN106407891A (en) * | 2016-08-26 | 2017-02-15 | 东方网力科技股份有限公司 | Target matching method based on convolutional neural network and device |
CN106504255A (en) * | 2016-11-02 | 2017-03-15 | 南京大学 | A kind of multi-Target Image joint dividing method based on multi-tag multi-instance learning |
CN107133955A (en) * | 2017-04-14 | 2017-09-05 | 大连理工大学 | A kind of collaboration conspicuousness detection method combined at many levels |
CN107330750A (en) * | 2017-05-26 | 2017-11-07 | 北京三快在线科技有限公司 | A kind of recommended products figure method and device, electronic equipment |
US20170330059A1 (en) * | 2016-05-11 | 2017-11-16 | Xerox Corporation | Joint object and object part detection using web supervision |
CN107392251A (en) * | 2017-07-26 | 2017-11-24 | 成都快眼科技有限公司 | A kind of method that target detection network performance is lifted using category images |
CN107437246A (en) * | 2017-07-05 | 2017-12-05 | 浙江大学 | A kind of common conspicuousness detection method based on end-to-end full convolutional neural networks |
-
2017
- 2017-12-08 CN CN201711295915.XA patent/CN108038502A/en active Pending
Patent Citations (10)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8885887B1 (en) * | 2012-01-23 | 2014-11-11 | Hrl Laboratories, Llc | System for object detection and recognition in videos using stabilization |
CN102982539A (en) * | 2012-11-09 | 2013-03-20 | 电子科技大学 | Characteristic self-adaption image common segmentation method based on image complexity |
CN103268607A (en) * | 2013-05-15 | 2013-08-28 | 电子科技大学 | Common object detection method on weak supervision condition |
US20170330059A1 (en) * | 2016-05-11 | 2017-11-16 | Xerox Corporation | Joint object and object part detection using web supervision |
CN106407891A (en) * | 2016-08-26 | 2017-02-15 | 东方网力科技股份有限公司 | Target matching method based on convolutional neural network and device |
CN106504255A (en) * | 2016-11-02 | 2017-03-15 | 南京大学 | A kind of multi-Target Image joint dividing method based on multi-tag multi-instance learning |
CN107133955A (en) * | 2017-04-14 | 2017-09-05 | 大连理工大学 | A kind of collaboration conspicuousness detection method combined at many levels |
CN107330750A (en) * | 2017-05-26 | 2017-11-07 | 北京三快在线科技有限公司 | A kind of recommended products figure method and device, electronic equipment |
CN107437246A (en) * | 2017-07-05 | 2017-12-05 | 浙江大学 | A kind of common conspicuousness detection method based on end-to-end full convolutional neural networks |
CN107392251A (en) * | 2017-07-26 | 2017-11-24 | 成都快眼科技有限公司 | A kind of method that target detection network performance is lifted using category images |
Non-Patent Citations (5)
Title |
---|
DENG-PING FAN 等: "Structure-Measure: A New Way to Evaluate Foreground Maps", 《2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION》 * |
FANMAN MENG 等: "A New Deep Segmentation Quality Assessment Network for Refining Bounding Box Based Segmentation", 《IEEE ACCESS》 * |
FANMAN MENG 等: "Weakly Supervised Part Proposal Segmentation From Multiple Images", 《IEEE TRANSACTIONS ON IMAGE PROCESSING》 * |
HONGLIANG LI 等: "Co-Salient Object Detection From Multiple Images", 《IEEE TRANSACTIONS ON MULTIMEDIA》 * |
WEN SHI 等: "Shape based co-segmentation repairing by segment evaluation and object proposals", 《2016 VISUAL COMMUNICATIONS AND IMAGE PROCESSING》 * |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111046917A (en) * | 2019-11-20 | 2020-04-21 | 南京理工大学 | Object-based enhanced target detection method based on deep neural network |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106547880B (en) | Multi-dimensional geographic scene identification method fusing geographic area knowledge | |
US10621185B2 (en) | Method and apparatus for recalling search result based on neural network | |
CN104881689B (en) | A kind of multi-tag Active Learning sorting technique and system | |
CN110633708A (en) | Deep network significance detection method based on global model and local optimization | |
AU2017101803A4 (en) | Deep learning based image classification of dangerous goods of gun type | |
CN109558902A (en) | A kind of fast target detection method | |
CN110287952A (en) | A kind of recognition methods and system for tieing up sonagram piece character | |
Cai et al. | Improving sampling-based image matting with cooperative coevolution differential evolution algorithm | |
Zhu et al. | A modified deep neural network enables identification of foliage under complex background | |
CN112712127A (en) | Image emotion polarity classification method combined with graph convolution neural network | |
CN111507285A (en) | Face attribute recognition method and device, computer equipment and storage medium | |
CN111582506A (en) | Multi-label learning method based on global and local label relation | |
CN105608457A (en) | Histogram gray moment threshold segmentation method | |
CN110580339A (en) | Method and device for perfecting medical term knowledge base | |
Wang et al. | Feature extraction and segmentation of pavement distress using an improved hybrid task cascade network | |
Jiang et al. | Tabcellnet: Deep learning-based tabular cell structure detection | |
Zhang et al. | CascadeGAN: A category-supervised cascading generative adversarial network for clothes translation from the human body to tiled images | |
CN108428234B (en) | Interactive segmentation performance optimization method based on image segmentation result evaluation | |
CN108845999B (en) | Trademark image retrieval method based on multi-scale regional feature comparison | |
Zhu et al. | EADD-YOLO: An efficient and accurate disease detector for apple leaf using improved lightweight YOLOv5 | |
CN109241315A (en) | A kind of fast face search method based on deep learning | |
CN107452003A (en) | A kind of method and device of the image segmentation containing depth information | |
CN108038502A (en) | Object collaborative detection method based on convolutional neural networks | |
CN112541010B (en) | User gender prediction method based on logistic regression | |
CN107563327B (en) | Pedestrian re-identification method and system based on self-walking feedback |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180515 |