DETECTING AND ANALYZING CORROSION SPOTS ON THE HULL OF LARGE MARINE VESSELS USING COLORED 3D LIDAR POINT CLOUDS

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1 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Senes, Volume III-3, 2016 XXIII ISPRS Congress, July 2016, Prague, Czeh Republ DETECTING AND ANALYZING CORROSION SPOTS ON THE HULL OF LARGE MARINE VESSELS USING COLORED 3D LIDAR POINT CLOUDS A. K. Ajaz a,b,, L. Malaterre a,b, M. L. Tazr a,b, L. Trassoudane a,b, and P. Chehn a,b, a INSTITUT PASCAL, Unversté Blase Pasal, Clermont Unversté, BP 10448, F Clermont-Ferrand, Frane b INSTITUT PASCAL, CNRS, UMR 6602, F Aubère, Frane kamalajaz@gmal.om; laurent.trassoudane@unv-bplermont.fr; paul.hehn@unv-bplermont.fr KEY WORDS: 3D LDAR pont louds, Deteton of defets, Shps ABSTRACT: Ths work presents a new method that automatally detets and analyzes surfae defets suh as orroson spots of dfferent shapes and szes, on large shp hulls. In the proposed method several sans from dfferent postons and vewng angles around the shp are regstered together to form a omplete 3D pont loud. The R, G, B values assoated wth eah san, obtaned wth the help of an ntegrated amera are onverted nto HSV spae to separate out the llumnaton nvarant olor omponent from the ntensty. Usng ths olor omponent, dfferent surfae defets suh as orroson spots of dfferent shapes and szes are automatally deteted, wthn a seleted zone, usng two dfferent methods dependng upon the level of orroson/defets. The frst method reles on a hstogram based dstrbuton whereas the seond on adaptve thresholds. The deteted orroson spots are then analyzed and quantfed to help better plan and estmate the ost of repar and mantenane. Results are evaluated on real data usng dfferent standard evaluaton metrs to demonstrate the effay as well as the tehnal strength of the proposed method. 1. INTRODUCTION Nowadays, there are lose to 6 mllon large shps n serve (Revew of Martme Transport, 2014), and all of them need to nspet, lean and pant ther hulls regularly (every 4-5 years) (Ortz et al., 2007). Tradtonal manual surveyng of shp hulls s ostly, tme onsumng and of lmted auray (Ortz et al., 2007) (Bskup et al., 2007). On the other hand, 3D sanners have emerged as a powerful tehnology soluton for many ndustres n reent tmes (Bskup et al., 2007), and pantng/reparng shps s not an exepton. Shp hull nspeton for detetng surfae defets suh as orroson for re-pantng and reparng purpose poses a number of hallenges, rangng from tme onsumpton due to large dmensons of the shp, lmted auray to poor llumnaton and lmted vsblty, et. Moreover, these defets ould be of any shape and sze and an be loated at any part of the hull. Deteton of these defets s urrently done manually by experts who nspet the hull and mark the areas to be treated/repared. Ths s a subjetve task whh makes t very dependent on the experene of person dong t and s also affeted by hs umulatve fatgue (Navarro et al., 2010). Alternatvely, laser sanners an operate n total darkness, n relatvely severe weather ondtons and provde fast 3D sans of the hull at hgh resoluton. These hgh resoluton sans an then be used to analyze the hull s surfae to detet these defets. The man am of ths work s to develop a method that uses ommerally avalable 3D laser sanners to san omplete shp hulls and then use these sans to automatally detet and analyze defets suh as orroson spots on the surfae of the shp hull. Ths wll not only help n redung the nspeton tme by many folds but also nreases the relablty as well as the auray of the deteton and estmaton of these defeted regons, as ompared to manual nspeton. As a result, ths ould lead to better optmzaton of dfferent repar and mantenane proesses savng mllons of dollars every year n the shppng ndustry. 2. RELATED WORKS Over the years, the task of detetng surfae defets has mostly been onsdered as a texture analyss problem as presented n (Fernández-Isla et al., 2013). In (Xe, 2008), texture analyss tehnques for detetng defets are lassfed nto four ategores: strutural approahes, statstal approahes, flter based approahes, and model based approahes. Whereas, n (Kumar, 2008), they are lassfed nto three: statstal, spetral, and model based. Ngan et al. (Ngan et al., 2011) dvde defet deteton methods largely nto nonmotf-based and motf-based approahes. The motf-based approah (Ngan et al., 2010) uses the symmetral propertes of motfs to alulate the energy of movng subtraton and ts varane among dfferent motfs for deteton. Many defet deteton methods rely on lusterng tehnques whh are manly based on texture feature extraton and texture lassfatons. These features are ollated usng methods suh as Fourer transform (Tsa and Huang, 2003), o-ourrene matrx (Han and Sh, 2007), Gabor transform (Kumar and Pang, 2002) and the wavelet transform (Ngan et al., 2005). The wavelet transform s an attratve opton when attemptng defet deteton n textured surfaes (Truhetet and Lalgant, 2008). In the lterature revew we fnd two man ategores of defet deteton methods based on wavelet transform. The frst ategory nludes dret thresholdng methods that rely on the fat that wavelet deomposton an attenuate texture bakground (Tsa and Huang, 2003) (Han and Sh, 2007). Ths allows the use of exstng defet detetng tehnques for non-textured mages, suh as (Sezgn and Sankur, 2004). Textural features extrated from wavelet-deomposed mages are another ategory whh s wdely used for defet deteton (Wong et al., 2009) (Ln, 2007). Features extrated from the texture patterns are used as feature vetors to feed a lassfer (Euldean dstane, Neural Networks, Bayer, or Support Vetor Mahnes). Ths has ertan lmtatons when handlng large mage data obtaned for dfferent nspeton tasks. The authors of (Zheng et al., 2002) presents a method based on mages of external metall surfaes usng an ntellgent approah Ths ontrbuton has been peer-revewed. The double-blnd peer-revew was onduted on the bass of the full paper. do: /sprsannals-iii

2 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Senes, Volume III-3, 2016 XXIII ISPRS Congress, July 2016, Prague, Czeh Republ based on morphology and genet algorthm to detet strutural defets on bumpy metall surfaes. The approah employs genet algorthms to automatally learn morphologal parameters suh as struture elements and segmentaton threshold, et. In (Armesto et al., 2011), a lght sweepng method for detetng defets on ar surfaes s presented. A seres of mages are obtaned whh are then merged together. After blurrng and mathng, defets appear as dark pxels after subtratng from the orgnal mage. However, the method has pratal lmtatons for large volumous objets/surfaes. A sensor system based on thresholdng tehnque s ntrodued n (Navarro et al., 2010), that s espeally suted for mage segmentaton under varable and non-unform lghtng ondtons. A global referene value s alulated usng denomnated Hstogram Range for Bakground Determnaton. Ths value s subsequently used to alulate the loal threshold of eah area of the mage, makng t possble to determne whether or not a pxel belongs to a defet. The drawbak of ths method s that the amera s optal axs should always be plaed perpendular to the plane of the surfae to be nspeted. Laser sannng as a new tehnology has been ntrodued n the marne ndustry for the last few years (Bskup et al., 2007). 3D sannng tehnology has begun to emerge n shpyards, but has not yet been exploted for the nspeton proess and speally deteton and analyss of defets on the hull. For these operatons, n reent years, a tendeny of mprovement appeared usng the tehnques of vson (Navarro et al., 2010). However, these solutons do not provde aeptable results n the ase of nspeton of very large surfaes, n varyng and non-unform lghtenng ondtons n the open ar (Navarro et al., 2010) (Zheng et al., 2002). Suh drawbaks and others an be overome n addton to hgh auray and sannng rates by usng 3D sannng tehnology. Bskup et al. (Bskup et al., 2007) used a terrestral laser sanner FARO LS 880 to model the hull and the dek of a shp. Analyss of data from the sanner was performed wth the ad of ommerally avalable software Geometr Studo 8. At the end, they get 3D model of two dfferentated parts (dek and hull) of the shp. Based on ths model, ertan seres of analyss ould be made as deteton of onstruton defets, possble asymmetres, along wth a varety of dfferent measures. However, surfae defets are stll not deteted. In ths work we present a new method of detetng and then analyzng surfae defets lke orroson on the shp s hull explotng data from a 3D sanner. Aordng to the best of our knowledge no pror work exsts that explots the 3D LDAR pont louds to detet and then analyze surfae defets on shp hulls. In the proposed method several sans from dfferent postons and vewng angles around the shp are regstered together to form a omplete 3D pont loud (Seton 3.). The R, G, B values assoated wth eah san, obtaned wth the help of an ntegrated amera are onverted nto HSV spae to separate out the olor omponent. Dfferent surfae defets suh as orroson spots of dfferent shapes and szes are automatally deteted, wthn a seleted zone, usng two dfferent methods dependng upon the level of orroson/defets (Seton 4.). The frst method reles on a hstogram dstrbuton whereas the seond on adaptve threshold based method. The deteted orroson spots are then analyzed and quantfed to help better plan and estmate the ost of repar and mantenane (Seton 5.). The results are evaluated on real data usng dfferent standard evaluaton metrs to demonstrate the utlty as well as the effay of the proposed method (Seton 6.). Conluson s presented n Seton DIGITIZATION OF SHIP HULL The dgtzaton of a omplete shp s hull, usng a statonary ground based LDAR sannng system, requres sannng from multple postons at approprate dstanes. For hgh resoluton sans, as neessary for our applaton, the dstane s kept nomnal wth slower sannng rates and large san overlaps. These multple sans after transformaton n a global frame of referene are regstered together and fltered to obtan a 3D pont loud of the omplete shp hull as shown n Fgure 1. The 3D sannng system, as shown n Fgure 1(b) has an ntegrated 2D amera whh allows a olored 3D pont loud.e. eah 3D pont wth ts assoated R, G & B values. (b) Fgure 1. shows the omplete 3D pont loud of the shp and the dokng area before the flterng phase. Eah 3D pont s oupled wth the orrespondng R, G & B value. (b) shows the 10 dfferent sannng postons (and dfferent vewng angles), of P20 laser sanner, used to san the omplete shp. Regstraton of Multple Sans In order to obtan multple sans the ground based LDAR sanner s plaed at dfferent postons all around the shp to ensure full overage at sutable resoluton. The sans are taken suh to ensure some overlappng to faltate the regstraton proess as shown n Fgure 1(b). In order to further ad the proess, addtonal targets are also plaed all around the shp. The sans are regstered, one by one, usng a standard ICP algorthm (Besl and MKay, 1992). In order to satsfy the equatons, t s ensured that at least 3 ommon targets are vsble n eah suessve san. 4. DETECTION OF CORROSION SPOTS One the 3D pont louds of the omplete shp s hull s obtaned after the regstraton step, orroson spots are deteted. As the 3D pont loud also ontans parts of the shp and surroundng other than the hull, we allow the user to manually selet a zone on the hull to be analyzed for orroson spots, usng a smple GUI as shown n Fgure 2. The deteton of orroson spots are then Ths ontrbuton has been peer-revewed. The double-blnd peer-revew was onduted on the bass of the full paper. do: /sprsannals-iii

3 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Senes, Volume III-3, 2016 XXIII ISPRS Congress, July 2016, Prague, Czeh Republ onduted wthn ths seleted zone automatally. Although t s possble to analyze the omplete shp s hull all at one, ths manual seleton of smaller zones of the hull (a ommon ndustral prate) allows the user/expert to qukly and effently dentfy the zone of nterest (wth larger hane of orroson spots) for further proessng and prevents wastng resoures on less nterestng parts. M AX M IN and H, S, V are the orrespondng pont of R, G, B, n the HSV spae. Also, to be noted that the norma lzed values of R, G, B are used,.e. R, G, B , and so as a result H and S, V In ase of R = G = B = 0, H s undefned, hene t s assumed to be 1. After the onverson, the olor omponent s then used n our analyss. Two dfferent tehnques are proposed to detet orroson spots dependng on the type of zone seleted. They are explaned as follows. 4.1 Hstogram based Deteton If the seleted zone has a majorty of non-orroded area as shown n Fgure 2 then a hstogram dstrbuton based method s used. Smlar to 2D mage segmentaton, the larger non-orroded surfae s onsdered as bakground wth orroson spots as foreground. A hstogram s obtaned for eah hannel of the olor omponent n the HSV spae. Based on these hstograms upper and lower bounds for the olor omponent ( = {H, S}) of the bakground regon (.e. non orroded regon) BU respetvely are au tomatally alulated. Based on the dstrbuton, the BU are alulated by analyzng the domnant peaks as shown n Fgure 3. Centered on the hghest peak, only the peaks wth more than 50% of ths maxmum heght are onsdered for the determnaton of the utoff regon (1)&(2). One the upper and lower bounds are determned, the 3D ponts belongng to the orroded regon are segmented usng (3), as shown n Fgure 4. (b) + Fgure 2. The two mages show the zones seleted by the user. shows a zone wth mnor orroson whereas (b) shows a zone wth domnant orroded area. BU = Hmax + m X Hbn (1) Hbn (2) As the orroson spots are usually more apparent as vsual defets (hange n olor, ntensty, et.) and less of physal deformaton (bendng, breakng, et.), the olor nformaton plays an mportant role n the deteton proess. Ths s also supported by the fat that usually the shp s hull tself s mono olor wth very lttle varaton and so orroson spots are easly vsble. As the R, G, B (Red, Green and Blue) olor values are prone to lghtnng varaton, they are onverted nto HSV (Hue, Saturaton and Value) olor spae, for eah 3D pont. Ths onverson separates the olor omponent from the ntensty omponent. Also, the ntutveness of the HSV olor spae s very useful beause we an quantze eah axs ndependently. Wan and Kuo (Wan and Kuo, 1996) reported that a olor quantzaton sheme based on HSV olor spae performed muh better than one based on RGB olor spae. The omponent, nvarant to the lghtenng ondtons, s then analyzed. It s referred to n ths paper as the olor omponent as t provdes more stable olor nformaton. Based on the desrpton presented n (Hughes et al., 2013), the followng equatons were used for the onverson. ( 0 h = H = h0 60, (G B) δ 2+(B R) δ 4+(R G) δ S= f R = M AX f G = M AX f B = M AX M AX M IN M AX, and V = M AX Here M AX = max(r, G, B), M IN = mn(r, G, B), δ = BL = Hmax m X ( P Non orroded regon f Corroded regon f P BU BL P > BU or P < BL (3) Here P s the th 3D pont n the seleted zone whle P value of ts olor omponent. Hmax s the value orrespondng to the maxmum peak n the dstrbuton, whle Hbn s the bn sze of the th peak for the olor omponent. m+ and m are the number of peaks onsdered n the utoff regon along the +ve and ve dretons as shown n Fgure 3. The 3D ponts belongng to the orroded regon are then lustered for further analyses as explaned n Seton Threshold based Deteton If the seleted zone s heavly orroded as shown n Fgure 2(b) then the hstogram based method s not useful as the predomnant bakground (.e. non orroded area) s not readly avalable and we annot base our model on the domnant orroded zone due to a lot of varaton n the olor omponents. Thus, we employ an adaptve threshold based method n whh a smaller sample vol ume s used to alulate the upper and lower bounds BU respetvely. Ths smaller sample volume of any non-orroded regon an be seleted from wthn the seleted zone or from any other part of the hull as the shp hull s manly the same (materal Ths ontrbuton has been peer-revewed. The double-blnd peer-revew was onduted on the bass of the full paper. do: /sprsannals-iii

4 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Senes, Volume III-3, 2016 XXIII ISPRS Congress, July 2016, Prague, Czeh Republ BL = Ps 3 p σs 2 (5) Here Ps s the mean value of the olor omponent of all the 3D ponts n the sample zone, whle σs 2 s the varane respetvely. The 3D ponts belongng to the orroded regon are segmented out as shown n Fgure 4 and further analyzed as explaned n the next seton. (b) () Fgure 4. shows the olored 3D pont loud of a small part of a seleted zone. (b) shows the orroded regon segmented out after deteton whle n () we fnd the 3D pont loud wth the orroded regon extrated out. (b) Fgure 3. and (b) show the hstogram dstrbuton for H and S respetvely for 3D ponts of a seleted zone. Based on the domnant peaks, BU are automatally seleted. and pant olor, et.) all around. The effet of llumnaton varaton on the olor values of 3D ponts on dfferent parts of the hull due to overlayng shadows, refletons, et., s already atered for by separatng the ntensty and the olor omponent by onvertng nto HSV olor spae and usng only the olor omponent for the analyss. One BU are alulated usng (4)&(5), the 3D ponts belongng to the seleted zone are segmented usng (3). p BU = Ps + 3 σs 2 (4) 5. ANALYSES AND ESTIMATION OF THE CORRODED REGION In order to analyze the orroded regon, the 3D ponts belongng to these regons are frst extrated out (as shown n Fgure 4) and then lustered together usng a k-means lusterng algorthm as presented n (Zhou and Lu, 2008). The ntal k lusters are seleted randomly and the algorthm mnmzes the dssmlarty measure between all 3D ponts and ther respetve assoated luster entrods. It uses the objetve funton defned as: J= k X n X kpj C j k2 j=1 =1 Ths ontrbuton has been peer-revewed. The double-blnd peer-revew was onduted on the bass of the full paper. do: /sprsannals-iii (6)

5 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Senes, Volume III-3, 2016 XXIII ISPRS Congress, July 2016, Prague, Czeh Republ where P j Cj 2 s a hosen dstane measure (Squared Euldean dstane) between a 3D pont P j and ts assoated luster entre C j. Ths dstane measures the dssmlarty of the 3D ponts to ther respetve assoated luster enters. Based on the lustered n 3D ponts, new entrods are estmated as the baryenters of the lusters and the proess s repeated untl the entrods seze to move. ((x x)(z z)) = B ((y y)(z z)) + C (z z) 2 (9) The result of the regresson s a plane that passes through a pont wth oordnates (x, y, z) and s returned n the form of a vetor normal to the best-ft plane. The equatons n (Press et al., 2007) are orreted to deal wth traes/resdue, by replang (7) wth the followng defnton: (y y) = A(x x) + C(z z) (10) and modfyng (8) and (9) aordngly. These 3D ponts ontaned n the reftted/ adapted voxel are then projeted on ths plane as shown n Fgure 5 and area s alulated as the area of the boundng retangle. The total surfae area s agan the sum of ndvdual areas as presented n (11) and (12). Fgure 5. Estmaton of V ol orr and Area orr for the 3D ponts of the orroded part ontaned n eah adapted Voxel. Estmaton of Corroded Regon One the 3D ponts are lustered, the total volume and surfae area of the orroded regons are determned. Ths helps n estmatng the amount of work and materal (suh as pant) requred for renovaton/overhaulng of the shp s hull. As the lusters formed of dfferent orroded regons are of varyng shapes and szes, t s dffult to alulate the exat surfae area and volume. Hene, we use a fnte element method by dvdng these regons nto fnte number of very small 3D voxels (Ajaz et al., 2013). Although the maxmum voxel sze s lmted to 5 m 3, the atual voxel sze vares dependng on the maxmum and mnmum values of the onsttutng 3D ponts along eah of the three axes as shown n Fgure 5. Ths way, a ertan orroded regon may be dvded nto tens or hundreds of smaller voxels dependng upon the densty and sze whle the adaptve voxel szes ensure that the profle of the regon s preserved. The volume s then smply alulated as the volume of the boundng ubod (reftted/adapted voxel). The total volume s then the sum of all ndvdual voxels as shown n (11). In order to alulate the surfae area, an arbtrary plane s determned by alulatng the best-ft plane through the 3D ponts ontaned n the reftted/adapted voxel usng planar regresson of data as presented n (Fernández, 2005). A best-ft plane s defned wth the equaton: (x x) = B(y y) + C(z z) (7) where x, y, and z are the respetve mean values of X, Y, and Z oordnates of all ponts. To fnd the equaton of the best-ft plane for a gven set of ponts, Press et al. (Press et al., 2007) present the followng equatons that are solved for B and C: ((x x)(y y)) = B (y y) 2 + C ((y y)(z z)) (8) V ol orr = N ( Xmax Xmn. Y max Ymn. Z max Zmn ) =1 Area orr = (11) N ( Xmax p X p p mn. Ymax Y p mn ) (12) =1 Here N s the total number of voxels, X, Y, Z and X P, Y P are the 3D and 2D-projeted oordnates of the ponts respetvely. As the voxel szes are very small, the volume and surfae area estmatons of the orroded regon, V ol orr and Area orr respetvely, are qute aurate. 6. EXPERIMENTS, RESULTS AND EVALUATION The proposed method was evaluated on real data D ponts were obtaned after sannng a shp (120 m long and 20 m wde) usng Lea s P20 laser sannng staton as shown n Fgure 1. Multple sans were obtaned from ten dfferent loatons, all around the shp, and then regstered together as explaned n Seton 3.. Dfferent zones on the shp s hull were seleted and the orroded regons were deteted and then analyzed usng our method. Some qualtatve results are presented n Fgure 6. The fgure shows that the method s able to suessfully segment out most of the orroded regons. As the method s non parametr (does not rely on partular shapes and szes) t s able to segment orroded regons of dfferent shapes and szes. For quanttatve results, some of these zones were manually segmented to obtan ground truth. The deteton results are presented n Table 1 usng dfferent standard evaluaton metrs as desrbed n (Vhnen, 2012). The analyss was onduted wth 3D ponts. Although, all these metrs are ommonly used to evaluate suh algorthms, MCC (Matthews Correlaton Coeffent) s regarded as most balaned measure as t s nsenstve to dfferent lass szes (lke n our applaton sometmes the number of ponts belongng to the orroded regons are sgnfantly less or more than that of the non-orroded regons). The MCC, lke the other measures, s alulated based on the ount of the true postves (.e. orret deteton of 3D ponts belongng to orroded regon), false postves, true negatves, and false negatves. A oeffent of +1 represents a perfet predton, 0 no better than random predton and 1 ndates total dsagreement. The detaled results nludng overall auray ACC and MCC greater than 88% and +0.6 respetvely, learly Ths ontrbuton has been peer-revewed. The double-blnd peer-revew was onduted on the bass of the full paper. do: /sprsannals-iii

6 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Senes, Volume III-3, 2016 XXIII ISPRS Congress, July 2016, Prague, Czeh Republ Table 1. metrs. Deteton results usng dfferent standard evaluaton Table 3. Effet of regstraton errors n the estmaton of V ol orr and Area orr. Metrs Results ACC Auray PPV Postve Predtve Value NPV Negatve Predtve Value FDR False Dsovery Rate F 1 F 1 measure MCC Matthews Coeffent Correlaton Table 2. Evaluaton of volume and surfae area estmaton. Ground Truth Estmated Error Zone V ol Area V ol Area V ol Area (m 3 ) (m 2 ) (m 3 ) (m 2 ) (%) (%) demonstrate the effay of the proposed method. It s also observed that the PPV s generally found hgher than NPV whh suggests that the method s more onservatve and s more lkely to detet orroded zones one hghly ertan otherwse t does not. Hene, false postves are usually less than false negatves. The estmaton of V ol orr and Area orr by the proposed method was also evaluated. Estmated values for fve dfferent zones were ompared wth the orrespondng ground truth. The ground truth was obtaned by manual labelng of orroded zones followed by Volume and Surfae Area alulaton based on the equal dstrbuton of 3D ponts usng a 3D CAD software. Ths supposton of equal dstrbuton s justfed by the fat that the szes of the seleted zones were relatvely small and the poston of the sanner was fxed. Ths ensures that there s mnmal to no varaton n the pont densty n the seleted zone. The results are presented n Table 2. The results show a relatvely small error whh demonstrates the effay of the method. Ths error dfferene between the estmated and the ground truth value also nludes the error due to some False Postves FP (zones wrongly deteted as orroded zones) as well as False Negatves FN (orroded zones not deteted). We also evaluated the effet of regstraton errors n the estmaton of V ol orr and Area orr. For ths analyss, multple sans of a partular zone were regstered wth small but dfferent regstraton errors. The results, presented n Table 3, show that regstraton errors play an mportant part n the deteton and analyss of orroded regons as ths effet the estmaton of the dmenson and magntude of the orroded regons. Ths may n turn result n hgher or lower estmaton of repar osts (n terms of materal requred suh as pant, man hours and other proesses, et.). 7. CONCLUSION In ths work a new method for automat deteton and analyss of surfae defets suh as orroson spots of dfferent shapes and szes, on large shp hulls s presented. In the proposed method multple sans from dfferent postons around the shp are regstered together to form a omplete 3D pont loud. The R, Regstraton Error V ol Area orr Area orr Zone (mm) (m 3 ) (m 2 ) (%) % % G, B values assoated wth eah san, are onverted nto HSV spae to separate out the llumnaton nvarant olor omponent from the refletane ntensty. Usng ths olor omponent, dfferent surfae defets suh as orroson spots of dfferent shapes and szes are automatally deteted, wthn a seleted zone, usng two dfferent methods dependng upon the level of orroson/defets. These deteted orroson spots are then analyzed and aurately quantfed. The dfferent aspets of the method are thoroughly evaluated on real data usng dfferent standard evaluaton metrs and the results learly demonstrates the effay as well as the applablty of the proposed method. Ths method not only helps to nrease the relablty but also the auray of the deteton and estmaton of these defetve regons, as ompared to manual nspeton that s urrently the standard prate. As a result, ths ould defntely lead to better estmaton of ost and optmzaton of dfferent repar and mantenane proesses n the shppng ndustry. ACKNOWLEDGMENTS The work reported n ths paper s supported and performed as part of the Frenh projet FUI-17 ROMAPE lead by SAMES TECHNOLOGIES, Frane. 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7 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Senes, Volume III-3, 2016 XXIII ISPRS Congress, July 2016, Prague, Czeh Republ (b) V olorr = 0.07 m3 Areaorr = 0.73 m2 () (d) V olorr = 0.40 m3 Areaorr = 4.60 m2 (e) (f) V olorr = 0.46 m3 Areaorr = 5.24 m2 (g) (h) V olorr = m3 Areaorr = 0.25 m2 Fgure 6., (), (e) & (g) show the olored 3D pont louds of dfferent zones whle (b), (d), (f) & (h) present the 3D pont louds wth the orroded regon segmented out along wth the orrespondng V olorr and Areaorr respetvely. Ths ontrbuton has been peer-revewed. The double-blnd peer-revew was onduted on the bass of the full paper. do: /sprsannals-iii

8 ISPRS Annals of the Photogrammetry, Remote Sensng and Spatal Informaton Senes, Volume III-3, 2016 XXIII ISPRS Congress, July 2016, Prague, Czeh Republ Kumar, A., Computer-Vson-Based Fabr Defet Deteton: A Survey. Industral Eletrons, IEEE Transatons on 55(1), pp Kumar, A. and Pang, G., Defet deteton n textured materals usng Gabor flters. Industry Applatons, IEEE Transatons on 38(2), pp Ln, H.-D., Automated vsual nspeton of rpple defets usng wavelet haraterst based multvarate statstal approah. Image and Vson Computng 25(11), pp Navarro, P., Iborra, A., Fernández, C., Sánhez, P. and Suardïaz, J., A sensor system for deteton of hull surfae defets. Sensors pp Ngan, H., Pang, G. and Yung, N., Performane Evaluaton for Motf-Based Patterned Texture Defet Deteton. Automaton Sene and Engneerng, IEEE Transatons on 7(1), pp Ngan, H. Y., Pang, G. K. and Yung, N. H., Automated fabr defet deteton a revew. Image and Vson Computng 29(7), pp Ngan, H. Y., Pang, G. K., Yung, S. and Ng, M. K., Wavelet based methods on patterned fabr defet deteton. Pattern Reognton 38(4), pp Ortz, F., Pastor, J. a., Alvarez, B., Iborra, A., Ortega, N., Rodríguez, D. and Conesa, C., Robots for hull shp leanng. In: IEEE Internatonal Symposum on Industral Eletrons, pp Press, W. H., Flannery, B. P., Teukolsky, S. A. and Vetterlng, W. T., Numeral repes 3rd Edton: The Art of Sentf Computng. 3rd edn, Cambrdge Unversty Press, New York, NY, USA. Revew of Martme Transport, PublatonsLbrary/rmt2014 en.pdf. Sezgn, M. and Sankur, B., Survey over mage thresholdng tehnques and quanttatve performane evaluaton. Journal of Eletron Imagng 13(1), pp Truhetet, F. and Lalgant, O., Revew of ndustral applatons of wavelet and multresoluton-based sgnal and mage proessng. Journal of Eletron Imagng 17(3), pp Tsa, D.-M. and Huang, T.-Y., Automated surfae nspeton for statstal textures. Image and Vson Computng 21(4), pp Vhnen, M., How to evaluate performane of predton methods? Measures and ther nterpretaton n varaton effet analyss. BMC Genoms 13(Suppl 4), pp. S2. Wan, X. and Kuo, C. J., Color dstrbuton analyss and quantzaton for mage retreval. In: Storage and Retreval for Image and Vdeo Databases (SPIE), pp Wong, W., Yuen, C., Fan, D., Chan, L. and Fung, E., Stthng defet deteton and lassfaton usng wavelet transform and BP neural network. Expert Systems wth Applatons 36(2, Part 2), pp Xe, X., A Revew of Reent Advanes n Surfae Defet Deteton usng Texture analyss Tehnques. Eletron Letters on Computer Vson and Image Analyss. Zheng, H., Kong, L. and Nahavand, S., Automat nspeton of metall surfae defets usng genet algorthms. Journal of Materals Proessng Tehnology , pp Zhou, H. and Lu, Y., Aurate ntegraton of multvew range mages usng k-means lusterng. Pattern Reognton 41(1), pp Ths ontrbuton has been peer-revewed. The double-blnd peer-revew was onduted on the bass of the full paper. do: /sprsannals-iii