A High-Accuracy Algorithm for Surface Defect Detection of Steel Based on DAG-SVM

Transcription

1 Sensors & Transducers, Vol. 57, Issue 0, October 203, Sensors & Transducers 203 by IFSA htt:// A Hgh-Accuracy Algorthm for Surface Defect Detecton of Steel Based on DAG-SVM, 2 Jl LU, Mngxng LIN, Yan HUANG, angtao KONG School of Mechancal Engneerng, Shandong Unversty, Jnan, Shandong 25006, Chna 2 School of Mechancal and Electronc Engneerng, Zaozhuang Unversty, Zaozhuang, 27760, Chna E-mal: mxln@sdu.edu.cn Receved: 4 October 203 /Acceted: 29 October 203 /Publshed: 3 October 203 Abstract: The qualty of the steel surface s a crucal arameter. An mroved method based on machne vson for steel surface defects detecton s roosed. The exerment s based on 20 mages for each of 6 dstnct steel defects, a total of 20 defectve mages acheved from the detecton system. 28 dfferent features are extracted from the mages and feature dmensons are reduced by the rncle comonent analyss (PCA) based on the samle correlaton coeffcent matrx. Herarchcal clusterng by Eucldean dstance s mlemented to fnd defect characterstcs dfferentaton, the steel surface defects are classfed based on drected acyclc grah suort vector machne (DAG-SVM). The exermental results ndcate that ths method can recognze more than 98 % of the steel surface defects at a faster seed that can meet the demands on the steel surface qualty onlne detecton. Coyrght 203 IFSA. Keywords: Machne vson, Surface defect detecton, Prncal comonent analyss (PCA), Drected acyclc grah suort vector machne (DAG-SVM), Dmenson reducton.. Introducton Steel s one of the most fundamental materals for automoble manufacturng machnery, shs, mltary defense, etc. However, there are some defects on the steel surface such as roller, bruses, edge crack, scratch, scar generated by durng roducng, rocessng, transortaton, loadng, unloadng, storage and many other reasons. These defects can not only affect the aearance of steel, but also cause steel scra, corroson, stress concentraton, easly to result n affectng steel erformance. So, how to detect these defects of steel effectvely and mlement real-tme detecton has become a sgnfcant subect. Surface defect detecton s an mortant alcaton n machne vson feld. General seakng, t needs a multle classfcaton of the surface detects detecton because there are many tyes of surface defects. The dffculty focuses on the desgn of the classfer. Currently, there are many notceable classfer methods such as Bayesan theory [, 2], K-means method [3], Fsher dscrmnant [4], K-Nearest Neghbor (KNN) [5], clusterng analyss [6, 7], etc. The classfcaton methods reresented by Bayesan theory and K-means are based on tradtonal statstcs by usng emrcal rsk mnmzaton n lace of exerental rsk mnmzaton, however, the devaton between the emrcal rsk mnmzaton and exerental rsk 42 Artcle number P_448

2 Sensors & Transducers, Vol. 57, Issue 0, October 203, mnmzaton acheves the theoretcal mnmum only when the tranng samle number tends to nfnty, whch s hard to meet n ractcal alcatons. In addton, these knds of method need to known the ror knowledge and model structure, whch s dffcult to acheve n ractce. Therefore, these methods can acheve good classfcaton n theory but not deal n engneerng alcaton. Suort vector machne (SVM) [8-2] consders both the emrcal rsk and the structural rsk to acheve best classfcaton wth less samles, so as to mrove the generalzaton ablty of the classfer. At the same tme, the method can solve the nonlnear classfcaton by ntroducng kernel functon, and the comlexty of calculaton deends on the dmenson of feature sace rather than the number of samles. In ths aer, a surface defect detecton system for steel late wth lnear CCD and monochromatc lght source s roosed accordng to the characterstcs of the steel late surface defect and the requrements of on-lne detecton. The DAG-SVM [3] decson tree s emloyed to detect the defects of steel, whch can reduce the structural rsk. In order to otmze the decson tree, the herarchcal clusterng method s chosen to otmze the desgn of the branches of tree structure accordng to the degree of dfferentaton n defect characterstcs, the selecton of arameters of 2-classfer n each node are dscussed corresondng to SVM decson tree. 2. CCD Detecton System The surface of measured steel late s wde and contnuous, tested contnuously and onlne. The t defect of the steel late surface s sngle color and consstent wth the measured steel surface but only color deth ncreases, these gve us a hnt that black and whte lnear CCD camera [4] can be chosen to reduce the amount of data rocessng and mrove the detecton seed. As a dffuse surface, the steel surface can scatter the lght and lead to reduce llumnaton ntensty, a monochromatc converged lght source wth hgh brghtness s used to mrove the lghtng brghtness. We desgn a detecton system as shown n Fg.. Suose the wdth of the measured steel surface s 800 mm, and lateral resoluton s 0.8 mm, two xels corresond to a detecton resoluton, one xel corresonds to 0.4 mm, then a steel surface wth a wdth of 800 mm needs 4500 xels. As shown n Fg., dual lnear CCD cameras wth 4096 xels are set u on a beam, and two LED lne lght sources are set u on other beam. To ensure the detecton area wthout omsson, a length of 00 mm overla detecton zone s retaned between the two cameras. As a result, the scannng length of each camera s 000 mm. The cameras are nstalled vertcally to ncrease the deth of feld of the mage system and ensure the acquston mage wthout dstorton. Lghts shne the camera scannng lne to detect lanar defects and ts defects smultaneously. In the case that lght ntensty meets the condtons, the llumnaton angle can be sutably reduced n order to ncrease the contrast of background wth dmles and facltate the detecton of t defects. 3. Defect Features Extracton 3.. Man Surface Defect of Steel Indentaton, bubble, ncluson, crack, stans and whte sot are 6 maor defects of steel. The characters of sngle color, agreement wth measured surface and only deth ncreased gve us a hnt that black and whte camera, monochromatc lght source can be chosen to reduce the amount of data rocessng and mrove the detecton seed. Surface defect of steel mages are shown n Fg. 2. a b c d e f Fg. 2. Man surface defects of steel. (a) mage wth whte sot, (b) mage wth bubble, (c) mage wth nclusons, (d) mage wth ndentaton, (e) mage wth crack, (f) mage wth stans. 3.2 Defect Segmentaton Fg.. CCD detecton system structure chart. Defect segmentaton s to searate the nterested defect targets from the background nformaton (such as color, outlne, brghtness, shae), whch s the 43

3 Sensors & Transducers, Vol. 57, Issue 0, October 203, foundaton of subsequent defect analyss and dscrmnaton. It s mossble for us to accurately dentfy the sze and quantty of the defects and make a rght evaluaton of surface qualty for steel f a defect target does not contan the whole defects. For examle, the ttng defect of steel s dvded nto several defect targets after edge segmentaton. So, we need a merge algorthm to combne all the targets whch belong to one defect nto a new and comlete defect. Consderng the characterstcs of steel surface features, we adot the defect segmentaton algorthm as shown n Fg. 3. Wavelet mult-scale edge detecton Edge connecton based on drecton Target contour udgment Target contours merge nto a comlete Rectangular corner extracton, obect Fg. 3. Defect segmentaton flowchart. For the defects shown n Fg. 2, we obtan the corresondng segmentaton results (as shown n Fg. 4) usng the method as shown n Fg. 3. a b c d e f Fg. 4. Results of defect segmentaton Dmenson Reducton and Features Selecton For steel surface defect detecton, t needs to further determne the tye of defect after segmentaton n order to meet the requrements of ractcal alcaton. The exerment studed the followng surface defect characterstcs of steel: geometrc features, gray level, roecton features and texture features, a total of 28 knds. Includng 8 knds of geometrc features whch descrbe the sze and shae of the defect, such as rectangularty, comactness, duty cycle, ovalty, eccentrcty, seven nvarant moments, number of boundary fttng straght lne and mnmum sloe, maxmum sloe, average sloe and sloe varance, etc. 27 knds of gray level features whch descrbe the gray dstrbuton, such as the maxmum, mnmum, average, varance, slantng degrees, kurtoss, energy, entroy of target gray level, gray scale gradent, edge gradaton; 40 knds of roecton features ncludng slantng, kurtoss, energy, entroy, waveform, ulse characterstcs, eak, margn characterstcs, average, varance of x- roecton, y-roecton, 45 roecton and 35 roecton; 43 knds of texture features manly ncludng the duty rato, waveform, ulse characterstcs, eak, margn characterstc, average, varance, slantng degrees, kurtoss, energy and entroy of the horzontal roecton and vertcal roecton of HH and LH resectvely. If these 28 knds of features were ut nto attern classfer drectly wthout any rocess, they wll reduce the effcency of attern classfer due to the too large dmenson of features. Therefore, we need to further choose the effectve characterstcs to reduce the dmensons. The feature dmensons are reduced by the rncle comonent analyss (PCA) based on the samle correlaton coeffcent matrx. Accordng to testng results we know that geometrc features are assocated wth edge length, area and shae, but not wth mage nsde nformaton; whle all other 0 tyes of features are related to mage content nformaton. So these 28 tyes of features are dvded nto two grous for rncal comonent analyss, 8 tyes of geometrc features and other 0 tyes of features. Suose (, 2,, ),,2,, n s a random samle wth caacty of n taken from (, 2,, ), thus we have the samle covarance matrx S and the samle correlaton coeffcent matrx R: n S ( s ) ( )( ) () k k n k R r ) s s s ( (2) Man features are selected by samle covarance matrx. Suose S ( ) s the samle covarance s matrx, the egenvaluess are 2 0, and the corresondng unt orthogonalzaton egenvalues are, 2,,, then we get the rncle comonent of th samle: 44

4 Sensors & Transducers, Vol. 57, Issue 0, October 203, , Y a a2 a,,2 (3) Then n observatons (,, ), k k k2 k ( k,2,, n) of are lugged nto one, n observatons ( ( k,2,, n) Y k rncal comonenty are obtaned, Var(Y ) α S α λ,, 2,, Cov(Y,Y ) α S α 0, Var( Y ) s λ Y one by ) of th (4) So the contrbuton rate of th samle s rncal comonent s gotten: Cov (5) And the accumulatng contrbuton rate of to m samles rncal comonents: Cov ~ m m (6) Dfferent dmensons may ncrease the dserson degree dfference of values, so we adot standardzed varables (samle correlaton coeffcent matrx) to elmnate the effects of dfferent dmensons. * 2 2 P P,,,,2,, n (7) s s22 s PP The samle covarance matrx after standardzaton s the samle correlaton coeffcent matrx R of orgnal data. We use the egenvalues of R and the corresondng unt orthogonal egenvectors and the rncal comonent analyss rocess abovementoned to obtan the samle rncal comonent after standardzaton. We choose 6 knds of defects, 20 mages of each defect, that total to 20 mages. Here, geometrc features, for examle, 8 knds of geometrc features can be gven n the form of equatons: v v v v v v v v B v v v v v v v v 2 k k 28 2 k k 208 (8) Here, element v k of B reresents egenvalue of the frst k geometrc features n mage. Usng the rncal comonent analyss based on samle correlaton coeffcent matrx above, we get egenvalues and accumulatve contrbuton rate of geometrc features, as shown n Fg. 5. Egenvalues Comonent analyss of geometrc features Egenvalues Contrbuton rate Accumulatve contrbuton rate Geometrc features Fg. 5. Comonent analyss of geometrc features From the data n Fg. 5, we can see that the accumulatve contrbuton rate of the to 5 egenvalues of samle covarance matrx has reached more than 90 %. Wth the accumulatve contrbuton rate of 90 % as a standard, accordngly, we can use the rncal comonents assocated wth the to 5 egenvalues nstead of the orgnal 8 varables. In ths way, the dmenson reducton rate can reach to 8/5=3.6, whle the nformaton s ket at a rate of 90 %. Therefore, the same method of the rncal comonent analyss based on correlaton coeffcent can be used for the subsequent 0 egenvalues. We can see that the accumulatve contrbuton rate of the to egenvalues of samle covarance matrx has reached 9.5 %. We can use the rncal comonents assocated wth the to egenvalues nstead of the orgnal 0 varables. In ths way, the dmenson reducton rate can reach to 0/=0, whle the nformaton s ket at a rate of 90 %, and greatly mrove the seed of the oeraton. 4. Defect Detecton 4.. DAG-SVM Decson Tree Drected acyclc grah suort vector machne (DAG-SVM) [3] s a new method of multle classfers combnaton of several bnary classfers. Ths method combnes the SVM and decson tree, constructs n ( n ) / 2 bnary SVM classfcatons to realze the mult-classfcaton. 6 tyes of man defect features are chosen accordng to the rncal comonent analyss based on the samle correlaton coeffcent. Because of the obvous dfference between geometrc features and gray level features, the features are dvded nto two Contrbuton rate 45

5 Sensors & Transducers, Vol. 57, Issue 0, October 203, arts (5 knds of geometrc features and knds of gray level features) that resectvely s calculated. The calculaton has two stes, the frst ste s to detect the resence of defects; the next s to classfy the surface defect based on DAG-SVM. In the classfcaton rocess, usng the DAG-SVM decson tree, samles are udged by the rncal comonent features. Those wth obvous dfferentaton n geometry are categorzed by geometry features whle others wthout obvous geometry features are ut nto gray classfer for classfcaton. In general, defects d, e, f that have obvous dfference n geometry features on the rncal comonent are classfed by ther geometrc features; defects a, b, c that have not obvous dfferences n geometry features on the rncal comonent are classfed further by ther gray features. The DAG-SVM decson tree of steel surface defects s shown n Fg. 6. Fg. 6. DAG-SVM decson tree of surface defect detecton Defect Features Clusterng In order to reduce the error rate of classfer and otmze desgn of classfer, we need to fnd the dstngushable dfferences of all the defect features, therefore, all defect samles cluster analyss n the ntal stage. In ths aer, the herarchcal clusterng method s adoted, usng the Eucldean dstance to measure the smlarty degree. Suose there are n samles wth ndctors, the Eucldean dstance between the samle and samle s: / 2 2 (, ) ( k k ) k d (9) The average dstance D ( G, Gq ) between class G and class G q s: D ( G, Gq ) d(, ) n n q G G q (0) The defect feature wth larger dstance s desgned as the uer nodes of DAG-SVM whle the defect feature wth smaller dstance as the lower nodes, to otmze the desgn of the DAG-SVM and reduce the accumulated errors Kernel Functon of SVM Due to steel has good dscrmnaton n surface defect features, we choose Gaussan radal bass functon (RBF) wth few arameters as kernel functon to reduce the comlexty of algorthm, ust determne the unsh arameter c and the kernel functon arameter g. In order to obtan hgher classfcaton accuracy, we use the K-fold cross valdaton (K-CV) method to adust arameters of classfer. Secfc mlementaton: the data set s dvded nto K subsets, one as a test set whle the rest of k- as tranng sets. Generally the data set s dvded randomly or equally. Parameters c and g take a varety of ossble values n a certan range randomly or accordng to ror knowledge to consttute a multle classfer. Then the tranng data set s ut nto the multle classfers, take the arameters wth hghest classfcaton accuracy s taken as the best arameters of c and g. However, there mght be more grous of c and g corresondng to the hghest classfcaton accuracy. The arameters of c and g wth mnmum value of c are taken as otmal arameters. If the mnmum value c corresonds to multle values g, t takes the frst retreved value g as the otmal arameter. Because arameter c s corresondng to the confdence range of the classfer, hgh confdence range can lead to exected rsks and over learnng, the tranng set has hgh classfcaton accuracy whle the test set has very low classfcaton accuracy. For ths reason, the smallest ossble arameters should be chosen on the remse of suffcent classfcaton accuracy. 5. Results and Dscusson 5.. Classfer Parameters For the arameters of classfers are determned by geometrc features and gray features. We choose 0 samles for each defect. There are 6 tyes of defects, a total of 0 6=60 tranng samles. Each has 5 geometrc characterstc values and gray characterstc values of rncal comonent analyss, all of whch forms a 60 6 data array as tran_data. Each samle has a category number, all of whch forms a 60 vector as tran_class. We can get the arameters n the subsequent classfers n the same way as the otmal arameters of classfer_ can be obtaned by the desgn deas of K-fold cross 46

6 Sensors & Transducers, Vol. 57, Issue 0, October 203, valdaton method. The otmal arameters of 5 classfers we get are shown n Table. Table. Parameters of classfers. Classfer Parameter c Parameter g Classfer_ Classfer_ Classfer_ Classfer_ Classfer_ Results of Classfcaton The abscssa reresents 60 test samles and the ordnate reresents 6 tyes of defects. Blue damonds n fgure reresent real classes of each test samle and red squares mean the results of classfcaton of the test samles. From Fg. 7 we can see that there s ust fault occurrng amongst 60 samles by usng our classfer, the classfcaton accuracy s above 98 % whch can meet the alcaton s demand. Tyes of defects f e d c b a Predcted test categorzaton Actual categorzaton Test samles 6. Conclusons Fg. 7. Defects classfcaton results. ) A new steel surface defect detecton method based on DAG-SVM s roosed. Ths method studes 6 tyes of steel defects, 20 mages for each defect, a total of 20 defectve mages, and extracts 28 tyes of features. Exermental results ndcate that ths method can recognze above 98 % surface defect of steel, whch can meet the demands of the steel surface qualty onlne detecton. 2) Feature dmensons are reduced by PCA based on the samle correlaton coeffcent matrx. To get a hgh comutatonal effcency and kee target nformaton as much as ossble, the geometrcal features and gray features are searated to reduce dmensons. For these defect data samles of steel, 6 rncal comonent arameters are ket after alcaton of rncal comonent analyss wth the dmenson reducton rate of 8:. 3) The DAG-SVM method s mlemented to classfy the surface defects of steel. A 5-classfer decson tree s desgned to classfy 6 tyes of steel defects. The K-fold cross valdaton (K-CV) method s emloyed to otmze the classfer arameters and obtan hgher classfcaton accuracy. Acknowledgements The authors gratefully acknowledge the suort of the natural scence foundaton (630242) and the natural scence foundaton of Shandong Provnce (ZR200EM037), the scentfc research foundaton for the returned overseas Chnese scholars, Sate Educaton Mnstry. References []. J. M. Bernardo, A. F. M. Smth, Bayesan theory, Wley, [2]. F. Pernkof, Detecton of surface defects on raw steel blocks usng Bayesan network classfers, Pattern Analyss and Alcatons, Vol. 7, Issue 3, 2004, [3]. T. Kanungo, D. M. Mount, N. S. Netanyahu, et al., An effcent k-means clusterng algorthm: analyss and mlementaton, IEEE Transactons on Pattern Analyss and Machne Intellgence, Vol. 24, Issue 7, 2002, [4]. M. Sugyama, Dmensonalty reducton of multmodal labeled data by local fsher dscrmnant analyss, The Journal of Machne Learnng Research, Vol. 8, 2007, [5]. L. Tomczak, V. Mosorov, D. Sankowsk, J. Nowakowsk, Image defect detecton methods for vsual nsecton systems, n Proceedngs of the 9 th Internatonal Conference The Exerence of Desgnng and Alcaton of CAD Systems n Mcroelectroncs (CADSM 07), art. No , 2007, [6]. A. Y. Ng, M. I. Jordan, Y. Wess, On sectral clusterng: analyss and an algorthm, Advances n Neural Informaton Processng Systems, Vol. 2, 2002, [7]. Fe He, Jn-Wu u, Zh-Guo Lang, et al., Hot rolled str state clusterng based on kernel entroy comonent analyss, Journal of Central South Unversty: Scence and Technology, Vol. 43, No. 5, 202, (n Chnese). [8]. Zong-Hua Zhang, Hong Shen, Alcaton of onlnetranng SVMs for real-tme ntruson detecton wth dfferent consderatons, Comuter Communcatons, Vol. 28, No. 2, 2005, [9]. S. Nashat, A. Abdullah, S. Aramvth, et al., Suort vector machne aroach to real-tme nsecton of bscuts on movng conveyor belt, Comuters and Electroncs n Agrculture, Vol. 75, Issue, 20, [0]. S. Ghora, A. Mukheree, M. Gangadaran, et al., Automatc defect detecton on hot-rolled flat steel roducts, IEEE Transactons on Instrumentaton and Measurement, Vol. 62, Issue 3, 203, []. Jng Ba, L-Hong Yang, ue-yng Zhang, An antnose SVM arameter otmzaton method for 47

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