CN108876781A - Surface defect recognition method based on SSD algorithm - Google Patents
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Abstract
The present invention provides a kind of surface defect recognition method based on SSD algorithm, includes the following steps：Handware defect image is acquired, defect image library is obtained；The defects of training set image input depth convolutional network is trained, obtains characteristics of image figure by construction depth convolutional network；Defects detection is carried out to characteristics of image figure, carries out error calculation；According to error update parameter, the deconditioning when error is less than setting error threshold obtains the depth convolutional network trained；The depth convolutional network that the input of the defects of test machine image has been trained is tested, judges whether image error meets the requirements；If so, the depth convolutional network that selection test image input has been trained is tested, recognition result is obtained.A kind of surface defect recognition method based on SSD algorithm provided by the invention carries out intelligentized identification to handware surface defect by training depth convolutional network, and high degree of automation effectively improves the efficiency to handware surface defects detection.
Description
Technical field
The present invention relates to Machine Vision Detection fields, more particularly to a kind of Surface Defect Recognition based on SSD algorithm
Method.
Background technique
Machine vision is also known as computer vision, is that research uses camera and computer to imitate human eye and brain respectively, with
Just it replaces people to detect and judge with machine, completes the science of the tasks such as target identification and industrial detection.Machine vision technique collection
A multi-disciplinary applied technology-oriented discipline such as Digital Image Processing, artificial intelligence, computer graphics is closed, in automatic metaplasia
It is widely used in production.In recent years, constantly improve with the progress of computer technology and neural network theory, has pushed computer
The fast development of vision.China's machine vision industry rapidly develops, and occupies in automated production detection field highly important
Status.
It is easily obtained since handware has many advantages, such as easy to form, light weight, material, is suitble to produce in enormous quantities, be in
The fields such as electricity, machinery, chemical industry, aviation are with very extensive.With more and more wider, the rapid shaping processing technology of application of handware
Development be getting faster, requirement of the people to the quality of handware is also higher and higher.The quality of handware mainly has size, appearance
Etc. requirement.Appearance is to guarantee an important link of handware quality, and artificial detection is generallyd use in actual production
Mode carry out.Manual detection mode inefficiency, the degree of automation be not high, and accuracy rate is often passed through with the work of testing staff
It tests related with attitude.Currently, handware production enterprise increasingly focuses on improving production automation level, to production efficiency
It is required that higher and higher, manual detection mode is increasingly unable to meet demand.In addition, in the process of production and processing, due to raw material object
Property Parameters variation, technological parameter be unreasonable and the factors such as processing equipment performance is bad, and hardware, which will appear, damages, sand holes, scrapes
Wound lacks material, deformation, point, greasy dirt etc. surface defect.These surface defects can not only destroy the appearance of hardware, but also
Will affect its performance lead to not using.The surface defects detection of current hardware and identification it is main manually based on,
It is inefficient, the degree of automation is low.
Summary of the invention
The present invention is to overcome existing handware by the way of artificial detection, and there are inefficiency, the degree of automation are low
The not high technological deficiency with accuracy provides a kind of surface defect recognition method based on SSD algorithm.
In order to solve the above technical problems, technical scheme is as follows：
A kind of surface defect recognition method based on SSD algorithm, includes the following steps：
S1：Handware defect image is acquired, defect image library is obtained；
S2：Defect image library is divided into training set and test set, defect type label is stamped on every image；
S3：The defects of training set image input depth convolutional network is trained, obtains image by construction depth convolutional network
Characteristic pattern；
S4：Defects detection is carried out to characteristics of image figure, testing result is obtained compared with label and carries out error calculation；According to error
Depth convolutional network parameter is updated, the deconditioning when error is less than depth convolutional network setting error threshold obtains having trained
Depth convolutional network；
S5：The depth convolutional network that the input of the defects of test machine image has been trained is tested, whether judges image error
It meets the requirements；If so, executing S7；If it is not, executing S3 re -training depth convolutional network；
S6：The depth convolutional network that selection test image input has been trained is tested, and recognition result is obtained.
Wherein, the defect image of handware is acquired in the step S1 by camera.
Wherein, in the step S2, defect image library is divided into training set and test set and accounts for defect image library picture number respectively
80% and 20%, each image all corresponds to the label of defective classification.
Wherein, in step s3, the depth convolutional network includes convolutional layer, excitation layer, pond layer；Wherein, the volume
Lamination, excitation layer have multiple, and convolutional layer connects excitation layer, and excitation layer is followed by convolutional layer, excitation layer connect again after convolutional layer, through excessive
After a continuous connection, excitation layer is connect with pond layer.
Wherein, the step S4 is specifically included：
S41：It is searched on low-dimensional characteristic pattern using small sliding window, by the defects of sliding window feature and the defect characteristic that in advance extracts
It is compared, obtains similarity, if similarity reaches the threshold value of setting, illustrate that the feature in sliding window belongs to lacking for a certain classification
Feature is fallen into, which includes the defect of a certain classification, to obtain image deflects；
S42：Obtained image deflects are compared with label, calculate one batch sample of input using batch gradient descent method
The error obtained afterwards carries out right value update to depth convolutional network parameter according to loss function, until error is less than the threshold of setting
Value exports the depth convolutional network trained.
Compared with prior art, the beneficial effect of technical solution of the present invention is：
A kind of surface defect recognition method based on SSD algorithm provided by the invention, by training depth convolutional network to hardware
Part surface defect carries out intelligentized identification, and high degree of automation effectively improves the effect to handware surface defects detection
Rate.
The present invention is also equipped with beneficial effect：Depth convolutional network is used in this algorithm, can extract number of drawbacks spy
Sign eliminates complicated image processing algorithm, by increasing network depth, can extract more features, improve defect point
The accuracy cut；It is identified on convolution characteristic pattern by search characteristics, the use of classifier is omitted, improves recognition efficiency,
Significantly reduce calculation amount.
Detailed description of the invention
Fig. 1 is the surface defect recognition method flow chart based on SSD algorithm.
Fig. 2 is depth convolutional network feature structure figure.
Fig. 3 is the region connection schematic diagram of hidden layer neuron and input layer 3X3.
Fig. 4 is the functional image of ReLU function.
Fig. 5 is pond layer workflow schematic diagram.
Fig. 6 is characterized schematic diagram when figure carries out defects detection.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
In order to better illustrate this embodiment, the certain components of attached drawing have omission, zoom in or out, and do not represent the ruler of actual product
It is very little；
To those skilled in the art, the omitting of some known structures and their instructions in the attached drawings are understandable.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
As shown in Figure 1, a kind of surface defect recognition method based on SSD algorithm, includes the following steps：
S1：Handware defect image is acquired, defect image library is obtained；
S2：Defect image library is divided into training set and test set, defect type label is stamped on every image；
S3：The defects of training set image input depth convolutional network is trained, obtains image by construction depth convolutional network
Characteristic pattern；
S4：Defects detection is carried out to characteristics of image figure, testing result is obtained compared with label and carries out error calculation；According to error
Depth convolutional network parameter is updated, the deconditioning when error is less than depth convolutional network setting error threshold obtains having trained
Depth convolutional network；
S5：The depth convolutional network that the input of the defects of test machine image has been trained is tested, whether judges image error
It meets the requirements；If so, executing S7；If it is not, executing S3 re -training depth convolutional network；
S6：The depth convolutional network that selection test image input has been trained is tested, and recognition result is obtained.
More specifically, the defect image of handware is acquired in the step S1 by camera.
More specifically, in the step S2, defect image library is divided into training set and test set and accounts for defect image library figure respectively
As the 80% of number and 20%, each image all corresponds to the label of defective classification.
More specifically, in step s3, the depth convolutional network includes convolutional layer, excitation layer, pond layer；Wherein, institute
State convolutional layer, excitation layer has multiple, convolutional layer connects excitation layer, and excitation layer is followed by convolutional layer, connects excitation layer after convolutional layer again, passes through
It crosses after multiple continuous connections, excitation layer is connect with pond layer.
More specifically, the step S4 is specifically included：
S41：It is searched on low-dimensional characteristic pattern using small sliding window, by the defects of sliding window feature and the defect characteristic that in advance extracts
It is compared, obtains similarity, if similarity reaches the threshold value of setting, illustrate that the feature in sliding window belongs to lacking for a certain classification
Feature is fallen into, which includes the defect of a certain classification, to obtain image deflects；
S42：Obtained image deflects are compared with label, calculate one batch sample of input using batch gradient descent method
The error obtained afterwards carries out right value update to depth convolutional network parameter according to loss function, until error is less than the threshold of setting
Value exports the depth convolutional network trained.
Embodiment 1
Depth convolutional network feature structure as shown in Figure 2, including convolutional layer, excitation layer and pond layer.Under normal conditions, neural
Input layer is indicated using a series of neurons in network, indicates more vivid with two-dimensional matrix in depth convolutional network
Intuitively.As conventional neural networks, the neuron needs of input layer are connected with the neuron of hidden layer.But depth convolution
Each input neuron is not connect by network with each hidden neuron, only in the partial zones of an image
Domain creation connection.
As shown in figure 3, by taking size is the image of 7X7 as an example, if the one of the neuron of first hidden layer and input layer
The region of a 3X3 connects.The region is called local sensing domain, 9 neurons in the local sensing domain and first hidden layer
The same neuron connects, and has a weight in each connection, therefore local sensing domain shares 3X3 weight.If by local
Domain is perceived along from left to right, sequence from top to bottom is slided, and will obtain neuron different in corresponding hidden layer, Fig. 3 is only
Show only the connection of first neuron and input layer of first hidden layer.A benefit is slided to the right in local sensing domain
It repays, is set as 2 here, will just input layer data and give second hidden neuron.It successively carries out, data can be completed from defeated
Enter the transmission of layer to hidden layer.3X3 neuron in first hidden layer obtained above all uses same 3X3 weight, this
Referred to as weight shares principle.In addition, each hidden neuron shares a deviation b, referred to as shared deviation.
The input of excitation layer neuron is similar with hidden layer input.Hidden layer data x is multiplied along with deviation b is obtained with weight w
It is inputted to excitation layer, i.e.,.By the value input stimulus function of y, ReLU function, functional image are selected here
As shown in Figure 4.
As shown in figure 5, pond layer uses max-pooling method, simplify the output of convolutional layer, the pond unit is simply
By the maximum excitation output in the input domain of a 2X2.
As shown in fig. 6, construction depth convolutional network carries out defects detection.Depth convolutional network mainly has convolutional layer, excitation
Layer, pond layer composition.Excitation layer is connected behind convolution, behind reconnect convolutional layer.By continuous several convolutional layers and excitation layer
A pond layer can be connect later.After such structure is repeated several times, depth convolutional network is just generated.
It is formed after depth convolutional network, defect image is inputted into depth convolutional network, then can be obtained by image
Characteristic pattern.It is searched on low-dimensional characteristic pattern using small sliding window, the defect characteristic that the feature in sliding window is extracted in advance is compared
Compared with obtaining similarity.If similarity reaches the threshold value being previously set, the feature being considered as in the sliding window belongs to a certain classification
Defect characteristic, the image include the defect of a certain classification.Mainly identify small defect, such as point on low-dimensional characteristic pattern, sand holes, this
Sample prevents network too deep, has desalinated small defect.Utilize big sliding window in characteristic pattern in last characteristic pattern of depth network
Upper search, main purpose are the big defects of detection, and such as greasy dirt lacks material, scratches.
Embodiment 2
In network training process, first the weight of network is initialized, and weight distribution is made to submit to mean value 0, variance
For 0.01 Gaussian Profile.So that number of the weight greater than 0 is approximately equal to the number less than 0 simultaneously, it is a certain to be beneficial to prevent network item
Whole handware images is divided into two major classes, i.e. training set and test set, accounts for 80% and 20% respectively by direction inclination.Every width figure
As all corresponding to a label, the i.e. classification of defect.The sample for having label input network is subjected to the training for having supervision, uses batch
Gradient descent method calculates the error obtained after one batch sample of input, according to the error update weight of loss function, until accidentally
Difference is less than the threshold value of setting.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (5)
1. a kind of surface defect recognition method based on SSD algorithm, which is characterized in that include the following steps：
S1：Handware defect image is acquired, defect image library is obtained；
S2：Defect image library is divided into training set and test set, defect type label is stamped on every image；
S3：The defects of training set image input depth convolutional network is trained, obtains image by construction depth convolutional network
Characteristic pattern；
S4：Defects detection is carried out to characteristics of image figure, testing result is obtained compared with label and carries out error calculation；According to error
Depth convolutional network parameter is updated, the deconditioning when error is less than depth convolutional network setting error threshold obtains having trained
Depth convolutional network；
S5：The depth convolutional network that the input of the defects of test machine image has been trained is tested, whether judges image error
It meets the requirements；If so, executing S7；If it is not, executing S3 re -training depth convolutional network；
S6：The depth convolutional network that selection test image input has been trained is tested, and recognition result is obtained.
2. the surface defect recognition method according to claim 1 based on SSD algorithm, it is characterised in that：The step S1
In by camera acquire handware defect image.
3. the surface defect recognition method according to claim 1 based on SSD algorithm, it is characterised in that：The step S2
In, defect image library is divided into 80% and 20% that training set and test set account for defect image library picture number respectively, and each image is all right
Answer the label of defective classification.
4. the surface defect recognition method according to claim 1 based on SSD algorithm, it is characterised in that：In step s3,
The depth convolutional network includes convolutional layer, excitation layer, pond layer；Wherein, the convolutional layer, excitation layer have multiple, convolutional layer
Excitation layer is connected, excitation layer is followed by convolutional layer, connects excitation layer again after convolutional layer, after multiple continuous connections, excitation layer
It is connect with pond layer.
5. the surface defect recognition method according to claim 1 based on SSD algorithm, it is characterised in that：The step S4
It specifically includes：
S41：It is searched on low-dimensional characteristic pattern using small sliding window, by the defects of sliding window feature and the defect characteristic that in advance extracts
It is compared, obtains similarity, if similarity reaches the threshold value of setting, illustrate that the feature in sliding window belongs to lacking for a certain classification
Feature is fallen into, which includes the defect of a certain classification, to obtain image deflects；
S42：Obtained image deflects are compared with label, calculate one batch sample of input using batch gradient descent method
The error obtained afterwards carries out right value update to depth convolutional network parameter according to loss function, until error is less than the threshold of setting
Value exports the depth convolutional network trained.
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| CN110176001A (en) * | 2019-06-03 | 2019-08-27 | 浙江大学 | A kind of high iron catenary insulator breakage accurate positioning method based on Grad-CAM algorithm |
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| CN109800697A (en) * | 2019-01-09 | 2019-05-24 | 国网浙江省电力有限公司舟山供电公司 | Transformer target detection and open defect recognition methods based on VGG-net Style Transfer |
| CN109800697B (en) * | 2019-01-09 | 2022-10-25 | 国网浙江省电力有限公司舟山供电公司 | Transformer target detection and appearance defect identification method based on VGG-net style migration |
| CN110197477A (en) * | 2019-05-07 | 2019-09-03 | 北京邮电大学 | The method, apparatus and system of pavement crack detection |
| CN110176001B (en) * | 2019-06-03 | 2021-09-03 | 浙江大学 | Grad-CAM algorithm-based high-speed rail contact net insulator damage accurate positioning method |
| CN110176001A (en) * | 2019-06-03 | 2019-08-27 | 浙江大学 | A kind of high iron catenary insulator breakage accurate positioning method based on Grad-CAM algorithm |
| CN110378618A (en) * | 2019-07-26 | 2019-10-25 | 杭州安脉盛智能技术有限公司 | Quality evaluating method and system based on online pipe tobacco surface defects detection |
| CN110598767A (en) * | 2019-08-29 | 2019-12-20 | 河南省收费还贷高速公路管理有限公司航空港分公司 | SSD convolutional neural network-based underground drainage pipeline defect identification method |
| CN111079628A (en) * | 2019-12-12 | 2020-04-28 | 哈尔滨市科佳通用机电股份有限公司 | Floor damage fault image identification method |
| CN111127417A (en) * | 2019-12-20 | 2020-05-08 | 江苏理工学院 | Soft package coil stock printing defect detection method based on SIFT feature matching and improved SSD algorithm |
| CN111127417B (en) * | 2019-12-20 | 2024-02-06 | 江苏理工学院 | Printing defect detection method based on SIFT feature matching and SSD algorithm improvement |
| CN111652227A (en) * | 2020-05-21 | 2020-09-11 | 哈尔滨市科佳通用机电股份有限公司 | Method for detecting damage fault of bottom floor of railway wagon |
| CN111652227B (en) * | 2020-05-21 | 2021-01-22 | 哈尔滨市科佳通用机电股份有限公司 | Method for detecting damage fault of bottom floor of railway wagon |
| CN113111911A (en) * | 2021-03-12 | 2021-07-13 | 东南大学 | Defect depth detection method based on principal component analysis and gate control circulation unit network |
| CN113111911B (en) * | 2021-03-12 | 2024-02-06 | 东南大学 | Defect depth detection method based on principal component analysis and gating circulation unit network |
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