CN106156765A - safety detection method based on computer vision - Google Patents

safety detection method based on computer vision Download PDF

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CN106156765A
CN106156765A CN201610782779.6A CN201610782779A CN106156765A CN 106156765 A CN106156765 A CN 106156765A CN 201610782779 A CN201610782779 A CN 201610782779A CN 106156765 A CN106156765 A CN 106156765A
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image
pedestrian
computer vision
safety detection
safety
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周宁宁
石少东
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Nanjing Post and Telecommunication University
Nanjing University of Posts and Telecommunications
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/53—Recognition of crowd images, e.g. recognition of crowd congestion
    • G—PHYSICS
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    • G—PHYSICS
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G06V10/507—Summing image-intensity values; Histogram projection analysis

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Abstract

The invention discloses safety detection method based on computer vision, be used for solving current safety detection demand persistency, high-precision problem.The method first carries out the gray processing of original image to original video data, and input picture carries out the standardization of color space, the contrast of regulation image, the impact that the shade of reduction image local and illumination variation are caused, the interference of suppression noise；Then describe the feature of pedestrian by histogram of gradients, classify in combination with SVM, after finding out pedestrian, then the behavior with degree of depth study analysis pedestrian, if the safety standard of not meeting, send safety alert information.The present invention can make full use of existing hardware equipment, and maximum possible decreases the change to original system.And the degree of depth learns it will be appreciated that the details of more image, has higher discrimination.Degree of depth learning neural network is less to the noise-sensitive such as environment, light sensitive, and once training can run under each environment, and generalization ability is good.

Description

Safety detection method based on computer vision
Technical field
The method that the present invention relates to image recognition, belongs to the applied technical field of Activity recognition based on computer vision, Purpose is to obtain stable, testing result accurately.
Background technology
Safety detection technology is a kind of emerging technology utilizing computer system to replace manually carrying out target detection, safety The process of detection be computer by getting video and video information being processed and analyzes, find solely in video image Vertical target, detects position, target area in follow-up video and is marked.Visual analysis is computer vision system One important process link of system.Computer vision relates to multiple fields such as image procossing, machine learning, pattern recognition, it Final goal be simulation people visual capacity, identification mission can be completed.At academia, from MIT, ETH Zurich, Visual analysis relevant issues are expanded positive with many scholars of Microsoft Research Deng Duojia research institution by WSU, Intel Explore.In industrial quarters, safety detection based on computer vision has been employed for screen monitoring, vehicle assistant drive, intelligence In the multiple application such as robot.
Along with the development of social safety with science and technology, it is increasingly required and scene is carried out video monitoring, according to regarding Frequently the information in image makes corresponding reaction.But, most monitor task has the features such as of long duration, high accuracy.For More efficient completes monitor task, uses the method for computer vision to become important research side of solution problem To, the most increasing scholar creates great enthusiasm to the detection of target behavior, also achieves huge progress.
Traditional pattern recognition typically uses two steps: feature extraction and tagsort.First, entrance spy it is originally inputted Levying extraction module, characteristic extracting module will be originally inputted patten transformation and obtain feature.Due to characteristic extracting module and concrete application Directly related, do not go to understand image from computer angle, the most extremely rely on engineer, considerably increase difficulty.Volume Long-pending neutral net is as a kind of feedforward neural network, it is possible to automatically learns the data having label in a large number, and therefrom extracts Complicated feature, only original image need to carry out a small amount of pretreatment just can identify visual pattern from pixel, and right More diverse identification object also has a preferable recognition effect, simultaneously convolutional neural networks identification ability not by image distortion or The impact of simple geometric transformation.Therefore use convolutional neural networks to carry out safety detection to provide stable, detect accurately Result, it is also possible to the problem found during detection is analyzed and classifies.
Summary of the invention
It is an object of the invention to provide the detection method that a kind of computer vision and the degree of depth learn to combine, work as solving Front safety detection demand persistency, high-precision problem.The method histogram of gradients (HOG) describes the feature of pedestrian, with Time combine SVM and classify.After finding out pedestrian, then the behavior with degree of depth study analysis pedestrian, finally provide analysis result.This The bright precision that significantly improves, and reduce required for manpower and hardware cost.
For solving above-mentioned safety detection demand persistency, high-precision problem, the technical scheme that the present invention proposes is a kind of Safety detection method based on computer vision, specifically includes following steps:
Step 1: user photographic head catches original video data；
Step 2: by original image gray processing, carries out the standardization of color space, the contrast of regulation image to input picture Degree, the impact that the shade of reduction image local and illumination variation are caused, the interference of suppression noise；
Step 3: calculate the gradient of each pixel of image, captures profile information, the interference of further weakened light photograph；
Every several cells are formed a block (block), by one by step 4: divide an image into little cells (cell) In individual block, feature descriptor (descriptor) of all cells is together in series, and produces the HOG feature of this block descriptor；
Step 5: HOG feature descriptor of all block in image image is together in series, generates this image HOG feature descriptor, and filter out image pedestrian's information with SVM (support vector machine) grader trained；
Step 6: the above-mentioned image pedestrian's information filtered out is input in degree of depth convolutional neural networks, pedestrian is gone For analyzing, if the safety standard of not meeting, send safety alert information.
Further, the standardization of color space described in above-mentioned steps 2 is to be implemented by Gamma (gal code) correction method.
Further, described in above-mentioned steps 3, gradient includes size and Orientation information.
Further, build the grader of convolutional neural networks described in above-mentioned steps 6 to comprise the steps of
1. set up data set；
2. set up convolutional neural networks, comprise the steps of
1) number of plies and structure are determined；
2) loss function is selected；
3) Dropout layer, and Dropout ratio are determined；
4) output layer equation is selected.
3. start to train neutral net, comprise the steps of
1) weight is initialized；
2) iteration termination condition is set.
Compared with prior art, the beneficial effects of the present invention is:
1, the present invention can make full use of existing hardware equipment, and such as photographic head and server etc., therefore this programme can be made Being that a plug-in unit is embedded in original system, maximum possible decreases the change to original system.
2, identification technology is compared with the machine learning algorithm of shallow-layer, and degree of depth study is it will be appreciated that the details of more image, energy Enough understand the relation between the safety helmet of shades of colour and human body, have higher discrimination.
3, degree of depth learning neural network is less to the noise-sensitive such as environment, light sensitive, and once training can be at each ring Running under border, generalization ability is good.
Accompanying drawing explanation
Fig. 1 is the structure chart of convolutional neural networks.
Fig. 2 is the method flow diagram of the present invention.
Detailed description of the invention
In conjunction with accompanying drawing, specific embodiments of the present invention are further described in detail.The present invention propose based on meter The safety detection method of calculation machine vision, first passes through photographic head and detection region is taked image information in real time, then to taking Each pixel of image carry out gradient calculation.With the support vector machines grader trained, entire image is entered Row traversal, obtains the information of picture position, pedestrian place, intercepts the top half of pedestrian's image, is input to the volume trained In long-pending neutral net, obtain the result of final safety behavior identification.
Method flow:
The present invention sets people daily pedestrian on-site and includes that two states, one are correctly to have worn safety helmet, separately Outer one is not have correct safe wearing cap, wherein, does not has correct safe wearing cap to include hand held for safety helmet this feelings Condition.
Refining the most further, safety detection method the method based on computer vision comprises the steps:
Step 1:
User photographic head catches original video data.
Step 2:
By original image gray processing, and with Gamma correction method, input picture is carried out the standardization (normalizing of color space Change) regulate the contrast of image, reduce the shade of image local and impact that illumination variation is caused, can suppress to make an uproar simultaneously The interference of sound.
Step 3:
Calculate the gradient (including size and Orientation) of each pixel of image；Primarily to capture profile information, enter simultaneously The interference of one step weakened light photograph.
Step 4:
Divide an image into little cells (such as 6*6 pixel/cell).By every several cell one block of composition (such as 3*3 cell/block), in a block, feature descriptor of all cell is together in series and just obtains this block's HOG feature descriptor.
Step 5:
HOG feature descriptor of all block in image image is together in series and can be obtained by this image HOG feature descriptor.And filter out pedestrian by the SVM classifier trained.
Step 6:
Image pedestrian's information is input in degree of depth convolutional neural networks, pedestrian is carried out behavior analysis.If do not met Safety standard then sends safety alert information.
As it is shown in figure 1, be one embodiment of the present of invention, it comprises the steps:
1. carry out feature extraction with HOG.
A. image gray processing, camera collection to RGB image coloured image gray scale turn to the black white image of 0-255.
B. use Gamma (I (x, y)=I (x, y)gamma) correcting algorithm carries out the standardization of color space to input picture, The contrast of regulation image, the impact that the shade of reduction image local and illumination variation are caused, suppresses noise simultaneously.
C. image gradient is calculated.
Calculate image abscissa and the gradient in vertical coordinate direction, and calculate the gradient direction value of each location of pixels accordingly. Pixel in image (x, gradient y) is:
Gx(x, y)=H (and x+1, y)-H (x-1, y)
Gy(x, y)=H (x, y+1)-H (x, y-1)
G in formulax=(x, y), Gy(x, y), (x y) represents pixel (x, y) horizontal direction at place in input picture to H respectively Gradient, vertical gradient and pixel value.(x, y) gradient magnitude and the gradient direction at place is respectively as follows: pixel
G ( x , y ) = G x ( x , y ) 2 + G y ( x , y ) 2
α ( x , y ) = tan - 1 ( G y ( x , y ) G x ( x , y ) )
D. image descriptor is built.
Dividing an image into little cells (such as 6*6 pixel/cell), we add up this 6*6 with 9 direction histograms The gradient information of pixel, is namely divided into 9 direction blocks by the gradient of cell.
Cell factory is combined into big block (block), normalized gradient rectangular histogram in block.In one block all of The characteristic vector of cell is together in series and just obtains the characteristic vector of this block, and they is used as svm classifier.
2. build SVM classifier.
Choose 1000 pedestrians as positive sample, and 1000 non-pedestrian are as negative sample, and normalize to 64*128 picture Element size.Obtain the Feature Descriptor descriptor of every width picture by the method in 1, so we just can reach 2000 parts and treat Two class data sets of training.We put into SVM classifier training by tagged for training set.Through several take turns training after we A model trained can be obtained.
3. build convolutional neural networks grader.
A. data set is set up.
Choosing 1200 pedestrian's photos that wear a safety helmet, intercept above the waist, the square picture cutting into 128*128 size is made For positive sample, then choose 1200 and there is no safe wearing cap pedestrian's photo, intercept as negative sample by same method.By sample Carrying out horizontal reflection, such data set can increase and is twice.We are by tagged for all of photo, and positive sample is 1, and negative sample is 0.And all of sample is upset, arbitrarily select 800 samples accomplishing fluently label as test sample.
B. the overall structure of convolutional neural networks structure.
As it is shown in figure 1, have 10 layers.Wherein three convolutional layers, each convolutional layer is followed by pooling layer, last Pooling layer is followed by the convolutional layer of a 1*1, is immediately a full hidden layer connected, and the output of full articulamentum is sent to one The Softmax layer of individual 2 dimensions, it produces a probability distribution covering two labels.Our network makes polytypic Logistic regressive object maximizes, and this is equivalent to maximise under prediction distribution the log probability of correct label in training sample Meansigma methods.
C. the selection of loss function.
C = - 1 n Σ x j [ y j lna j L + ( 1 - y j ) l n ( 1 - a j L ) ] + λ 2 n Σ w w 2
Wherein n represents the example number that training set comprises, and λ ＞ 0 represents regularization parameter, and y represents sample Actual value, x represents that input value, w represent the weight of Current Situation of Neural Network, and a represents the value that neutral net forward-propagating is calculated. We encourage smaller weight to reduce sound pollution by deducting weight in proportion.
D. the initialization of weight.
Set up an each layer of Fig. 1 neutral net and have the RBM neutral net of identical input dimension and same depth, we Data set is put in RBM neutral net and is trained, after 100 take turns training, weight that we train every layer and Deflection is taken out, and is put in convolutional neural networks corresponding position.
E. before neutral net output layer one layer we select Dropout to reduce overfitting.
When meeting iterated conditional:
Step1: hidden layer randomly selects the neuron of half and deletes；
Step2: carry out forward on the neutral net deleted and reversely update；
Step3: the neuron deleted before recovery, repeats Step1, Step2, until being unsatisfactory for iterated conditional.
Each neuron in the neutral net finally learning out is to learn on the basis of only half neuron. At the end of iteration, we halve all of for hidden layer weight.The hidden layer that Dropout throws away half the most at random is neural Unit, is equivalent to us and is trained in different neutral nets, decreases the dependency of neutral net, the most each nerve Unit cannot rely upon certain or other neuron several, forces neural network learning and other neurons to be joined together more Strong.
F. the output equation Softmax of output layer.
It is dangerous that final output needs to be divided into two classes, i.e. behavior safety and behavior.The previous neuron of output layer defeated Go out forThe output equation below of last layer of output layer is calculated by weSafe and unsafe probability, we use that output of maximum probability as testing result.
G. process is trained.
Using stochastic gradient descent method and a collection of size is 128, and power is 0.9, and weight decays to the sample of 0.0005.Right More new regulation in the w of weight is
v i + 1 = 0.9 &CenterDot; v i - 0.0005 &CenterDot; &epsiv; &CenterDot; w i - &epsiv; &CenterDot; < &part; L &part; w | w i > D i
wi+1=wi+vi+1
Wherein i is iterations, and v is dynamical variable, and ε is learning rate.
First stage, forward propagation stage:
Sample (X, a Y is taken from sample setp), X is inputted network；
Calculate corresponding actual output Op.In this stage, information through conversion step by step, is sent to output from input layer Layer.The process that this process performs when to be also network properly functioning after completing training.In the process, what network performed is meter Calculate (actually the input weight matrix phase dot product with every layer, obtains last output result):
Op=Fn(…(F2(F1(Xpw(1))w(2))…)w(n))
Second stage, the back-propagation stage:
A) reality output O is calculatedpWith corresponding preferable output YpDifference
B) power is adjusted by the method back propagation of minimization error
Termination condition:
Two conditions can be chosen and meet end training neutral net: 1. iteration meets the wheel number of certain setting；2. work as Learning rate is less than the numeral set the stable wheel number in setting.
The foregoing is only a specific embodiment of the present invention, not in order to limit the present invention, all essences in the present invention Within god and principle, any modification, equivalent substitution and improvement etc. made, should be included within the scope of the present invention.

Claims (4)

1. safety detection method based on computer vision, it is characterised in that the method comprises the steps:
Step 1: catch original video data with photographic head；
Step 2: by original image gray processing, carries out the standardization of color space, the contrast of regulation image, fall to input picture The impact that the shade of low image local and illumination variation are caused, the interference of suppression noise；
Step 3: calculate the gradient of each pixel of image, captures profile information, the interference of further weakened light photograph；
Every several junior unit lattice are formed a block, by all little lists in a block by step 4: divide an image into junior unit lattice The feature descriptor of unit's lattice is together in series, and produces the HOG feature descriptor of this block；
Step 5: be together in series by the HOG feature descriptor of all pieces in image, generates the HOG feature descriptor of this image, And filter out image pedestrian's information with the support vector machine classifier trained；
Step 6: the above-mentioned image pedestrian's information filtered out is input in degree of depth convolutional neural networks, pedestrian is carried out behavior and divides Analysis, if the safety standard of not meeting, sends safety alert information.
Safety detection method based on computer vision the most according to claim 1, it is characterised in that: described in step 2 The standardization of color space is implemented by gamma correction method.
Safety detection method based on computer vision the most according to claim 1, it is characterised in that: described in step 3 Gradient includes size and Orientation information.
Safety detection method based on computer vision the most according to claim 1, it is characterised in that: in construction step 6 The grader of described convolutional neural networks comprises the steps of
(1) data set is set up；
(2) set up convolutional neural networks, comprise the most again following sub-step:
A. the number of plies and structure are determined；
B. loss function is selected；
C. Dropout layer, and Dropout ratio are determined；
D. output layer equation is selected.
(3) start to train neutral net, comprise the most again following sub-step:
A. weight is initialized；
B., iteration termination condition is set.
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CN106781198A (en) * 2016-12-31 2017-05-31 马宏林 A kind of kitchen pre-alarm system
CN106909887A (en) * 2017-01-19 2017-06-30 南京邮电大学盐城大数据研究院有限公司 A kind of action identification method based on CNN and SVM
CN107454364A (en) * 2017-06-16 2017-12-08 国电南瑞科技股份有限公司 The distributed real time image collection and processing system of a kind of field of video monitoring
CN108012121A (en) * 2017-12-14 2018-05-08 安徽大学 A kind of edge calculations and the real-time video monitoring method and system of cloud computing fusion
CN108021926A (en) * 2017-09-28 2018-05-11 东南大学 A kind of vehicle scratch detection method and system based on panoramic looking-around system
CN108319934A (en) * 2018-03-20 2018-07-24 武汉倍特威视系统有限公司 Safety cap wear condition detection method based on video stream data
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CN109726652A (en) * 2018-12-19 2019-05-07 杭州叙简科技股份有限公司 A method of based on convolutional neural networks detection operator on duty's sleep behavior
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CN106781198A (en) * 2016-12-31 2017-05-31 马宏林 A kind of kitchen pre-alarm system
CN106909887A (en) * 2017-01-19 2017-06-30 南京邮电大学盐城大数据研究院有限公司 A kind of action identification method based on CNN and SVM
CN107454364A (en) * 2017-06-16 2017-12-08 国电南瑞科技股份有限公司 The distributed real time image collection and processing system of a kind of field of video monitoring
CN107454364B (en) * 2017-06-16 2020-04-24 国电南瑞科技股份有限公司 Distributed real-time image acquisition and processing system in video monitoring field
CN110832505A (en) * 2017-07-04 2020-02-21 罗伯特·博世有限公司 Image analysis processing with target-specific preprocessing
CN108021926A (en) * 2017-09-28 2018-05-11 东南大学 A kind of vehicle scratch detection method and system based on panoramic looking-around system
CN108012121A (en) * 2017-12-14 2018-05-08 安徽大学 A kind of edge calculations and the real-time video monitoring method and system of cloud computing fusion
CN110119656A (en) * 2018-02-07 2019-08-13 中国石油化工股份有限公司 Intelligent monitor system and the scene monitoring method violating the regulations of operation field personnel violating the regulations
CN108319934A (en) * 2018-03-20 2018-07-24 武汉倍特威视系统有限公司 Safety cap wear condition detection method based on video stream data
CN108460358A (en) * 2018-03-20 2018-08-28 武汉倍特威视系统有限公司 Safety cap recognition methods based on video stream data
CN109726652A (en) * 2018-12-19 2019-05-07 杭州叙简科技股份有限公司 A method of based on convolutional neural networks detection operator on duty's sleep behavior
CN109919182A (en) * 2019-01-24 2019-06-21 国网浙江省电力有限公司电力科学研究院 A kind of terminal side electric power safety operation image-recognizing method
CN109919182B (en) * 2019-01-24 2021-10-22 国网浙江省电力有限公司电力科学研究院 Terminal side electric power safety operation image identification method
CN110135514A (en) * 2019-05-22 2019-08-16 国信优易数据有限公司 A kind of workpiece classification method, device, equipment and medium
CN110717466A (en) * 2019-10-15 2020-01-21 中国电建集团成都勘测设计研究院有限公司 Method for returning position of safety helmet based on face detection frame
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Application publication date: 20161123