Automated Rust Defect Recognition Method Based on Color and Texture Feature

Transcription

1 Automatd Rust Dfct Rcognition Mthod Basd on Color and Txtur Fatur L. M. Chang, H. K. Shn 2, and P. H. Chn 3 Profssor, Dpartmnt of Civil Enginring, National Taiwan Univrsity, Taipi, Taiwan 2 PhD Candidat, Dpartmnt of Civil Enginring, National Taiwan Univrsity, Taipi, Taiwan 3 Associat Profssor, Dpartmnt of Civil Enginring, National Taiwan Univrsity, Taipi, Taiwan Abstract - Digital imag procssing has bn applid to divrs industry disciplins. Th wid application of digital imag procssing can b attributd to th following advantags: accuracy, objctivity, fastnss, consistncy, and th mixtur of thm. Although th RUst Dfct Assssmnt(RUDA) mthod [] was fficintly usd to diffrntiat dfctiv imags from non-dfctiv imags on blu color coat, its ffct on othr colors or undr spcific nvironmntal conditions has not yt bn xplord. A nw stl bridg rust dfct rcognition mthod which hybrids Fourir Transform and Color Imag Procssing is proposd in this papr for automatically rcognizing rust dfcts and adapting to various background colors. Kywords: Coating Dfct, Fourir Transform, Nonuniform Illumination, Pattrn Rcognition Introduction A traditional way of assssing th coating quality of a stl bridg is to dtrmin th ara prcntag of rust dfcts through th tmplat of th corrosion prformanc rating providd by th Amrican Socity for Tsting and Matrials (ASTM). Howvr, it is difficult to dtrmin rust zon and quantify th prcnt ratio by nakd ys. Thrfor, digital imag procssing mthod is usd to objctivly dtrmin th rust dfct and its corrsponding prcntag. In North Amrica, imag procssing mthod for assssing stl bridg coating dfcts has bn usd sinc lat 990s [-4]. Most of prviously dvlopd imag rcognition mthods for assssing quality of stl bridg surfac coating focusd on calculating th ara prcntag of rust dfcts. Howvr, rror could happn on computing th rust prcntag in imags that actually contain no rust as shown in Figur. In ordr to avoid such misjudgmnt, th xistnc of stl bridg coating rust dfcts should b rcognizd bfor th calculation of th rust prcntag [5]. (a) (b) Figur. (a) A blu coating imag contains no rust; (b) Th corrsponding rust assssmnt with no rust on (a). L usd statistical analysis to build th stl bridg coating dfct rcognizd approach. This pionring approach workd ffctivly with blu color imag of th coating takn on th bridg in th Stat of Indiana, U.S.A.. But, th ffcts on othr colors of th coating or undr spcific nvironmntal condition, such as non-uniform illumination, wr rmaind to b furthr xplord. Knowing th challng lft in th prior rsarch fforts, a nw mthod hybrids Fourir transform and color imag procssing is proposd to idntify rust dfcts automatically, handl non-uniform illumination and ffctivly rcogniz rust for various color of coatings. Th purpos of this papr is to prsnt th hybrid mthod. 2 Color fatur and txtur fatur In ordr to improv th accuracy of digital imag procssing, a propr mthod should b slctd basd on th fatur of imags. Th most commonly usd imag faturs ar th shap, color, and txtur [6-6]. Most of th prviously dvlopd mthods sgmnt imags and rcogniz rust by th colors in th imags. Color imags ar firstly convrtd into gray-scal imags bfor procssing du to th limitation of computr calculating spd. Th systm wr oftn ncountrd difficultis whil acquiring digital imags undr particular nvironmntal conditions such as non-uniform illuminations, low-contrast digital imags, and noiss on painting surfacs [5].

2 To solv this problm, color imags wr analyzd dirctly du to computr capabilitis dvloping, and indd, this mthod had bttr prformanc than prvious ons. Othr litraturs showd th corrlation btwn th bits of color and th lvl of compltion to dpict colors [7]. A gray imag is 8-bit whras a color imag normally has 24 bits lngth. Thrfor, color imags provid mor information for analysis, and solv problms that gray imags cannot dal with. In this study, 9 color spac wr collctd, including Gray-scal, RGB rgb II2I3 HSI HSV YUV YIQ YCbCr YCgCr CIE XYZ CIE W*U*V* CIE L*u*v* CIE L*a*b* CIE L*C*h* CMY Xrox YES ATD Kodak YCC[4, 8-22], in ordr to find out a comparativly propr color configuration for th rust rcognition of stl bridg imags. Howvr, th prformanc of this mthod was not satisfid whn th coating color of a stl bridg is clos to rd or brown. Aftr analyzing th stl bridg coating imags, it was found that th diffrncs within a background ar not as big as that btwn th background and th rust. Th lvl of diffrncs btwn background and rust could b a usful indx for diffrntiating th two from txtur faturs. In som cass, faturs of th imags ar difficult to b obtaind in spac domain but asy to b xtractd in frquncy domain. Fourir Transform is on of th commonly usd analysis mthods in frquncy domain. Th currnt rport usd 2-D discrt Fourir transform (DFT) for analyzing two dimnsions digital imags. Th quation for th DFT is dfind in quation (), whr f (x,y) rprsnts a digital imag of siz M x N, and th valus of th discrt variabls u and v in th rangs ar u=0,,2,,m- and v=0,,2,,n- [22]. F u, v f x, y u M N x 0 y 0 0,,2,..., M, v ux vy j2 M N 0,,2,..., N 3 Fourir-transform-basd mthod for coating dfct rcognition In ordr to rul out th influnc of non-uniform illumination, a Fourir-Tansform-basd mthod was () proposd to rcogniz stl bridg coating dfcts, calld Fourir-Transform-Dfct Dtction Approach (FT-DEDA) [23]. First, th acquird imags wr convrtd into singl color componnt. Scond, th 2- D discrt Fourir transform of th imags was computd, and thn multiplid by lowpass filtr. Third, th invrs transform of th rsult was calculatd, and finally, compar it with original imag. Th imag would b judgd as a dfctiv imag if diffrncs appar btwn th original imag and th calculatd rsult. It indicats th xistnc of high frquncis which ar normally gnratd from othr componnts in th imag such as rust. On th othr hand, if th imag was a plain background, thr should b no diffrncs btwn th original imag and th rsult. In this study, Gaussian lowpass filtr (GLPF) was usd to distinguish low frquncis from high frquncis. Th quation of GLPF in two dimnsions is givn as quation (2). H u, v 2 D u, v 2 2D0 (2) Whr D(u,v) is th distanc btwn point (u,v) and th cntr of th frquncy rctangl. D0 is a constant. W can adjust th D0 valu of GLPF to chang th scop which nds to b filtrd out. In ordr to find th bst combination of th D0 valu of GLPF and th color componnt, artificial imags wr gnratd to valuat th prformanc of th systm in this study. Th imags wr convrtd into fifty-fiv color componnts, rspctivly. 2-D discrt Fourir transform of th imags was thn computd, and multiplid by Gaussian lowpass filtr. Th cutoff frquncy D0 of GLPF was adjustd basd on th imag siz, which ar %, 2%,, 9%,0%,20%,,70%, and 80% of th short sid of th imag, rspctivly. Thn w invrsly transformd th rsults and compard thm with original imags. Basd on th procssd rsults, th imags would b judgd as dfctiv or non-dfctiv. Finally, th bst combination of th D0 valu and th color componnt would b obtaind according to th xprimnt rsults. Th xprimntal stup of dvlopmnt for th automatd stl bridg dfct dtction approach is shown in Figur 2.

3 Tabl 2. Th prformanc of th combination of th D0 valu and th color componnt in artificial nonuniform illuminatd imags. th combination of th D0 Accuracy valu and th color componnt 99.4% b*(l*a*b*), D0=4%, 5% b(rbg), D0=6% b(rbg), D0=7% 99.3% b(rbg), D0=8% V(LUV), D0=6% 99.% V(LUV), D0=5% Figur 2. Th xprimnt stup of dvlopmnt for th automatd stl bridg dfct dtction approach. In this study, 40 normal condition artificial rust imags and 560 non-uniform illuminatd ons wr usd to dvlop an automatd stl bridg coating dfct rcognition approach. Th artificial imags wr convrtd into 55 kinds of color componnts and matchd with 7 kinds of D0. Thus, it could rsult in 935 combination from ach imag. Tabl shows th prformanc of th combination of th D0 valu and th color componnt xamind in th 68 artificial imags, including 28 pur background imags, in normal condition. Thr appard svn combinations with 00% Accuracy. Tabl. Th prformanc of th 7 combinations of th D0 valu and th color componnt in artificial imags. th combination of th D0 valu Accuracy and th color componnt r(rbg), D0=7% V(UVW), D0=4% V(UVW), D0=5% 00% V(LUV), D0=4% V(LUV), D0=5% b*(l*a*b*), D0=4% b*(l*a*b*), D0=5% Tabl 2 shows th prformanc of th combination of th D0 valu and th color componnt xamind in 672 artificial imags, involving 2 pur background imags, in non-uniform illuminatd condition. Th bst combinations to idntify dfcts of artificial nonuniform illuminatd imags ar: th D0, 4% of th short sid of th imag, and b* componnt of L*a*b* color spac; and th D0, 5% of th short sid of th imag, and b* componnt of L*a*b* color spac. Considring ths rsults from artificial imags and artificial non-uniform illuminatd imags in our xprimnts, th bst combination to xtract th pixls of dfct ar: th D0, 4% of th short sid of th imag, and b* componnt of L*a*b* color spac; and th D0, 5% of th short sid of th imag, and b* componnt of L*a*b* color spac. Th D0 valu of GLPF rgulats th thrshold of frquncis, blow which th frquncis could pass th filtr. Th smallr D0 valu indicats mor high frquncis bing gatd out. Howvr, small D0 could rjct th frquncis of non-homognous background, rsulting in misjudging th imag as dfctiv. Although smallr D0 valu could lvat systmatical snsitivity and rais th probability of misjudging subjcts as dfctiv imag, th imags still hav chanc to b rviwd in th follow-up procdur. On th contrary, th systm would stop whn th imags wr judgd as non-dfctiv imags and thr would b no chanc to modify th rsults in this cas. To avoid misjudgmnt, this rport chos a rlativly small D0 valu of GLPF, 4% of th short sid of th imag, and b* componnt of L*a*b* color spac, to obtain a consrvativ rcognition rsult. This study applid color imag procssing and Fourir transform to dvlop an automatd stl bridg coating dfct rcognition approach. First, th acquird imags wr convrtd into b* componnt of L*a*b* color spac. Thn th 2-D discrt Fourir transform of th imags was computd and multiplid by GLPF with D0 valu, 4% of th short sid of th imag. Nxt, w applid Similarity S, prsnting th diffrnc btwn th invrsly transformd rsult and th original imag. If th S smallr than 00%, th imag was judgd as a dfctiv imag. On th othr hand, if th S is 00%, th imag was judgd as a non-dfctiv imag. Th procss of th automatd stl bridg dfct rcognition approach is shown in Figur 3.

4 6 Dfctiv Dfctiv Dfctiv Dfctiv 8 Dfctiv Dfctiv 9 Dfctiv Dfctiv Figur 3. Procss of th automatd stl bridg dfct dtction approach. Figur 4 shows th procssd rsults of imags acquird undr normal conditions, th gnral conditions with no spcific nvironmntal influncs such as non-uniform illumination and noiss on painting surfacs. Itm to 3 ar dfctiv imags and itm 4 to 0 ar non-dfctiv imags with rd, blu, brown, grn, gray, purpl, and whit color. Th computr for this validation procssing was providd with a Intl(R) Cor(TM)2 Quad CPU Q9400@ 2.66GHz and 4.0 GB RAM. itm 2 Input imag Imag Th rsult of FT-DEDA status S Classification Tim (s) Dfctiv Dfctiv Dfctiv Dfctiv 0 Dfctiv Dfctiv Figur 4. Procssd rsults of dfcts imags acquird undr normal conditions. According to Figur 4, FT-DEDA classifid all th dfctiv imags corrctly but has misjudgd a nondfctiv imag, itm 5. Comparing to itm 5, which hav th sam blu coating as itm 6, itm 6 was rcognizd as non-dfctiv by FT-DEDA. It indicatd that blu color was not th rason of misjudgmnt. Figur 5 indicats th dtaild analysis of th imag itm 5. Th diffrnt pixls btwn th invrsly transformd rsult and th original imag ar shown in Figur 5a and th corrsponding positions on th acquird imag to th diffrnt pixls ar shown in Figur 5b (th rd circl spots th diffrnt pixls). Th spottd ara has biggr intnsity chang and could b transformd into frquncy sssion that highr than th rst part of th imag. This xprimnt rvald th limitation of txtur fatur, which could not procss th non-dfctiv imags with prominnt objcts. This would b corrctd in th follow-up procdur. 3 Dfctiv Dfctiv 4 Dfctiv Dfctiv 5 Dfctiv Dfctiv (a) (b)

5 Figur 5. Th dtail analysis of th imag itm 5 (a) Diffrnt pixls btwn th invrsly transformd rsult and th original imag; 5 (b) th corrsponding positions on th acquird imag to th diffrnt pixls. 4 Stl bridg coating rust dfct rcognition approach Th stl bridg coating dfcts wr dtctd by FT- DEDA as dscribd in th last sssion didn t corrspond to th rust pixls xactly, and thy might indicat othr componnts in th imag lik nois on th painting surfac or non- homognous background painting. In this sssion, w analyzd th colors of rust in th stl bridg to distinguish othr objcts from rust. 4. Imag sgmntation bas on color fatur Th main chmical compounds consisting rust ar ithr FOOH or F2O3.3H2O [24]. Bcaus th color diffrnc btwn or within th two compounds is not significant, it is possibl to dfin th rust in a spcific color rang by its color fatur. In ordr to dfin an appropriat color rang for rust, w collctd a st of sampl color points of rust. W thn valuatd th avrag color of th st. Th avrag would b th cntr of th spcific color rang. By dtrmining whthr th pixl is within th spcific color rang or not, w could classify ach pixl in an imag [22]. 4.2 Dfinition of th Rust Color In this rsarch, w collctd svral bridg imags and cut off rust ara by nakd ys. Figur 6 prsnts xampls of rust aras. Pixls of ths rust aras wr collctd in rust databas. Th color spctrum of th rust databas is shown in Figur 7. Ths data hav bn collctd as a spcifid color rang to dfin th color rang of rust. spctrum was dpictd by man and varianc in quation (3) to (4). Th valus along th R axs rangd from 64 to 247; th valu along th G axs rangd from to 89; th valu along th B axs rangd from 0 to 48. Th acquird point in th stl bridg coating imag was classifid as rust if it was insid th spcifid color rang. man (R, G, B) = (93.54, 24.25, 59.6) (3) standard dviation (R, G, B) = (29.95, 30.5, 22.3) (4) Figur 8. Scattr of th rust color spctrum in RGB spac. Th prominnt objcts (Figur 5a) which wr misjudgd in th last sction and rust color spctrum wr convrtd into scattr plots of RGB color spac as shown in Figur 9. Th grn dots rprsntd th pixls of rust color spctrum and th rd dots wr th prominnt objcts. Th man and standard dviation of prominnt objcts in RGB spac wr: m= (9.53, 44.00, 74.93), s= (24.06, 3, 20.95). Th valu of R, G, and B axs vary from 8 to 77, 84 to 203, and 40 to 26, rspctivly. Figur 9. Scattr of th prominnt objcts (Figur 5a) and rust color spctrum. Figur 6. Exampls of rust aras. Figur 7. Color spctrum of rust databas. W dtrmind that th prominnt objcts wr not rust dfct bcaus th rd dots wr not within th rang of grn dots. Th rsult corrspondd with th imag status by human vision, not rust dfctiv imag. According to th scattr of th rust color spctrum in RGB spac (Figur 8), th avrag color of rust color

6 4.3 Modl building 5 Exprimnt rsult Although th automatd stl bridg coating dfct dtction approach (FT-DEDA) provids a mthod to dtct dfcts in th imags, thr is still a shortag. It cannot rcogniz th rust dfcts from othr dfcts. Thrfor, charactristic of rust color was applid to build th nw proposd modl, calld a RUst DEfct Rcognition Approach (RUDERA) for stl bridg surfac coating imags basd on color. Th RUDERA modl was volvd from th automatd stl bridg coating dfct dtction approach (FT-DEDA) and color sgmntation tchniqu basd on color. First, w applid FT-DEDA to dtct th xistnc of dfcts in th imags. If th imag was judgd as a non-dfctiv imag, th procdur was stoppd at this stp. On th contrary, if th imag was judgd as dfctiv, th dfcts pixls wr scattrd and th distanc btwn dfcts pixls and th spcifid color rang of rust color spctrum wr calculatd. Th imags wr judgd as a rustdfct imag whn th dfcts pixls wr insid th rang of rust color spctrum. Othrwis, th imags would b judgd as an othr-dfct imag. Th procss is summarizd in Figur 0. This sction shows th powr of RUDERA, and compars it with RUDA proposd by L in Basd on L s dissrtation, th RUDA was rprogrammd[5] and xprimntd in this study. Figur indicats 0 rcognition rsults of RUDERA and RUDA, rspctivly. Th fild imag status prsnts th classification rsults of th input imags by nakd ys. Itm to 3 ar dfctiv imags and th itm 4 to 0 ar non-dfctiv imags with rd, blu, brown, grn, purpl, and whit color. Itm 8 to 9 ar imags undr non-uniform illumination conditions, and Itm 0 is a non-dfctiv imag having a bug on th painting surfac. Itm Input imag Imag status Th rsult of RUDERA Classific ation Rust- Dfctiv Dfcts 2 Dfctiv Rust- Dfcts Tim (s) Th rsult of RUDA Classific Tim ation (s) 0.5 Dfctiv Dfctiv 3 Dfctiv Rust- Dfcts 0.5 Dfctiv Dfctiv Othr- Dfcts 0.5 Dfctiv 0.08 Dfctiv 0.06 Dfctiv Dfctiv 0.05 Dfctiv Dfctiv 0.08 Dfctiv Dfctiv Dfctiv Dfctiv 0.08 Dfctiv Dfctiv Dfctiv 0.08 Dfctiv Dfctiv 0.08 Dfctiv 0.08 Figur 0. Procss of th automatd stl bridg rust dfct rcognition mthod.

7 0 Dfctiv Othr- Dfcts 0.4 Dfctiv 0.08 Figur. Rcognition rsults of RUDERA and RUDA undr normal conditions. According to Figur, it is clar that RUDERA classifid all th imags corrctly, and it can b applid in a wid varity of color lik rd, blu, brown, grn, purpl, and whit color. Morovr, it can procss th coating imags with prominnt objcts (.g. Itm 5), which FT-DEDA cannot dal with and was not influncd by th bug on th painting surfac (.g. Itm 0). In our xprimnts, th rcognition rsults support th conclusion of L s dissrtation, which works ffctivly in th blu color. Apart from blu color, RUDA also has good prformanc in th gray, black, blu, grn, purpl, and whit color. Th capability of rcognition from good to bad was RUDERA > RUDA, and th procssing tim from long to short was RUDERA >RUDA. Although RUDERA consumd mor tim, it is imprcptibl and can adapt to a wid varity of coating colors and rul out th influnc of non-uniform illumination and th nois on painting surfacs. 6 Conclusions A rust dfct rcognition approach for stl bridg surfac coating imags basd on color and txtur fatur (RUDERA) was proposd by using Fourir Transform and color imag procssing mthod in ordr to rcogniz th xistnc of rust dfcts bfor assssing th ratio, adapt to various background colors and ovrcom th influncs from particular nvironmntal conditions, including nonuniform illumination and noiss on painting surfacs. Th RUDERA modl was volvd from th Fourirtransform-basd mthod for automatd stl bridg coating dfct dtction approach (FT-DEDA) and color sgmntation tchniqu basd on color. Th main ida of FT-DEDA was to tak advantag of th diffrnc within a background pixl ar not as big as th diffrnc within a rust pixl, and thn distinguishd th two groups by Fourir transform. Furthrmor, FT-DEDA could ovrcom th problm of non-uniform illumination bcaus illumination could b managabl through Fourir transform. Although FT-DEDA could dtct dfcts in th imags, it could not rcogniz th rust dfcts from othr dfcts. To idntify rust dfcts, RUDERA taks advantag of th color diffrnc btwn rust compounds that ar not significant to diffrntiat th rust dfct from othrs. Th xprimnt dmonstrats that th proposd mthod could adapt to various colors of stl bridg coating, spcially workd whn th coating color is clos to rd or brown which prvious mthod cannot procss. It ovrcam th influncs from 2 particular nvironmntal conditions, non-uniform illumination and noiss on painting surfacs. Morovr, it could classify th stl bridg surfac coating imags into thr groups, dfctiv, Rust-dfctiv, and Othr-dfctiv. Thrfor, th proposd mthod has bttr diffrntial capacity than th prvious ons on stl bridg dfct rcognition. 7 Rfrncs [] L, S., L.M. Chang, and M. Skibniwski, Automatd rcognition of surfac dfcts using digital color imag procssing. Automation in Construction, (4): p [2] AbdlRazig, Y.A. and L.-M. Chang, Intllignt Modl for Constructd Facilitis Surfac Assssmnt. Journal of Construction Enginring and Managmnt, (6): p [3] Chn, P.H. and L.M. Chang, Intllignt stl bridg coating assssmnt using nuro-fuzzy rcognition approach. Computr-Aidd Civil and Infrastructur Enginring, (5): p [4] Yang, Y.C., Smart Color Imag Rcognition for Stl Bridg Rust Inspction, in Dpartmnt of Civil Enginring Collg of Enginring. 2009, National Taiwan Univrsity. p. 72. [5] L, S., Digital color imag procssing systm for civil infrastructur halth assssmnt and monitoring: Stl bridg coating cas. 2005, Dissrtation, Purdu Univrsity: Unitd Stats -- Indiana. [6] Frman, H., On th Encoding of Arbitrary Gomtric Configurations. Elctronic Computrs, IEEE Transactions on, 96. EC-0(2): p [7] Haralick, R., K. Shanmugam, and I.H. Dinstin, Txtural Faturs for Imag Classification. IEEE transactions on systms, man, and cybrntics, (6): p

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