Computer Vision System for Weld Bead Analysis

Luciane Baldassari Soares, Atila Astor Weis, Bruna de Vargas Guterres, Ricardo Nagel Rodrigues

and Silvia Silva da Costa Botelho

Science Computer Center - C3, Federal University of Rio Grande - FURG, Brazil

Keywords:

Computer Vision, Discontinuities, Welding.

Abstract:

Welding processes are very important in different industries and requires precision and attention in the steps

that will be performed. This article proposes the use of an autonomous weld bead geometric analysis system

in order to verify the presence of geometric failures that may compromise the weld integrity. Using an vision

system attached to a linear welding robot, images of pre-welded and post-welded metal plates are captured and

compared and metrics are applied for evaluation. The proposed method uses Hidden Markov Model (HMM)

to identify the weld bead edges and calculate several evaluation metrics to detect geometric failures such as

misalignment, lack or excess of fusion, among others.

1 INTRODUCTION

The Welding process consists of a fundamental

practice in several areas such as automotive industry,

metallurgy, civil construction, naval industry and rai-

lway. The welding process is considered laborius due

to the risks the welder is exposed to, such as sparks,

toxic gases, radiation emitted by the arc and electri-

city. For this reason, technological advances in this

area not only provide productivity increase and pro-

cess enhancement, but also assure the welder secu-

rity. Besides the mentioned risks, the welder must

adjust the parameters, guide the torch and control the

weld bead quality (Broering et al., 2005). As a con-

sequence, the weld quality and the productivity may

be compromised since the operator can become over-

worked.

Welding processes represent a very signiﬁcant

part of the manufacturing costs of a product. The-

refore a weld bead fault not identiﬁed during the qua-

lity inspection process can have major consequences

for the equipment manufacturer (Andreucci, 2013).

Due to all above mentioned factors, the use of hu-

man operated welding robots is increasingly common.

Additionally, these robots reduce the rework and pro-

vide a more efﬁcient production line. In the naval in-

dustry, for example, it is indispensable to use more

ﬂexible solutions since it is necessary to weld metal

sheets for different sized ships.

In shipyards, an alternative to automate the wel-

ding process is the Bug-O Matic Weaver robot, shown

Figure 1: Bug-O Matic Weaver Robot - Source: Author.

in Figure 1. The system is remotely operated, decrea-

sing the welder contact with the risk area. The robot,

positioned on a rail attached to any linear surface, car-

ries the torch during the welding process. It is posible

to set different parameters such as weaving regime

(arm oscilatory moviment) and robot and arm linear

velocity.

It is worth noting the predominant welding techni-

que used in shipyards is the FCAW (Flux Cored Arc

Welding). The FCAW has several advantages being

widely used in naval industry which aims to increse

the productivity keeping the quality of the processes

(Machado, 2015).

The automation of the welding process makes it

not only more stable, but also more uniform, thus re-

ducing the risks of discontinuities and avoiding the

weld bead discard (Carvalho et al., 2009). Further-

more, to manually achieve this quality level it would

402

Soares, L., Weis, A., Guterres, B., Rodrigues, R. and Botelho, S.

Computer Vision System for Weld Bead Analysis.

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP, pages

402-409

ISBN: 978-989-758-290-5

be necessary a welder with years of experience.

Nowadays, the weld bead inspection is performed

not only by Non-Destructive Testing (NDT) inclu-

ding visual methods, penetrating liquid, magnetic par-

ticles, radiography and ultrasound, but also by De-

structive Evaluation such as tensile, impact, folding,

fatigue and hardness tests (Lima et al., 2005). Accor-

ding to (Andreucci, 2013), the solution to assist the

quality control of equipments and materials is related

to NDT techniques. As reported by the Brasilian As-

sociation of Non-Destructive Testing and Inspection

(ABENDI), the NDT techniques are one of the main

quality control tools, they increase the weld reliability

and reduce the process cost (Abendi, 2014).

It is important for the realized tests the identiﬁca-

tion of discontinuities such as incomplete or excessive

fusion, welding bite, porosity, incomplete or exces-

sive penetration, cracks and geometrical weld bead

irregularities that may compromise the weld quality.

Although it is possible to extract some relevant infor-

mation about the weld bead geometry, we still do not

have a tool able to identify all presented discontinui-

ties.

The computer vision may be the solution to

quickly and precisely identify some of the above dis-

continuities. In conclusion, computational techniques

are studied and applied in order to stablish an auto-

matic and ideal solution for the inspection and de-

tection of discontinuities on the weld seam (Felis-

berto, 2010).

2 LITERATURE REVIEW

In the works (Cook et al., 1995; Hou and Liu, 2012;

Kumar et al., 2012; Kumar et al., 2014) it is pre-

sented methods ofﬂine for welding quality inspection

and evaluation using digital cameras and lighting sy-

stem. More classic approaches are carried out in

(Cook et al., 1995) and (Hou and Liu, 2012). It is

assumed that the weld width (which can be identi-

ﬁed through the horizontal integration) and the spa-

cing between the undulations (which can be obtained

through the peak-to-peak distance) can not oscillate

for good weld quality. Moreover Hou et al. makes

use of the Canny algorithm for identifying the edges

of the weld bead. After the edges are identiﬁed, they

are plotted in 2D histogram, in order to compare them

with histograms of images of good quality weld be-

ads. Through a pre-established threshold the weld is

classiﬁed good or not.

In the article of Kumar et al. (Kumar et al.,

2012)(Kumar et al., 2014) an evaluation system for

MIG (Metal Inert Gas) welding in “V” groove is pro-

posed. Four frames are captured in sequence and then

the regions of interest of the image are segmented and

some characteristics based on the average intensity

of the pixels are extracted. In (Kumar et al., 2012),

the authors use a back-propagation neural network to

classify the weld in four categories: good, excessive,

insufﬁcient and without weld. The system was tested

with 80 samples and shows 95% accuracy in the clas-

siﬁcation. Yet, in (Kumar et al., 2014) the same clas-

siﬁer was used but with improved feature extraction,

based on a chi-square test to verify compliance with a

Gaussian distribution of pixels. This increased preci-

sion to 96.25%.

In the articles on (Schreiber et al., 2009) and (Sun

et al., 2005) the visual inspection methods are applied

online, that is, they are performed during the welding

process. In these methods the camera is usually pla-

ced next to the torch, obtaining an image of the re-

cently welded bead. The biggest obstacle to online

processing is the acquisition of images due to condi-

tions in the welding environment such as arc bright-

ness, smoke, sparks, welding spatters and reﬂections.

The works of Schreiber et al. and Sun et al. use

training techniques and pattern recognition, so that

the algorithm learns the good and bad characteristics

of a weld bead. Schreiber et al. presents a system

that consists of two distinct phases: training and ve-

riﬁcation. In the training phase the system learns the

quality criterion required from the training performed

through images of weld beads considered to be well

welded, examined by a welding engineer. This step

should be performed only once before using the ro-

bot and consists of extracting measurements from the

images of the training weld beads. These measures

are combined in order to obtain reference values and

tolerance ranges.

The veriﬁcation step occurs by comparing each

frame obtained from the image of the welding being

performed, with the reference frames obtained from

the weld beads of training. In this process the evalu-

ated characteristics are the position and the width of

the cord, as well as the distribution of light around the

arc.

Sun et al. proposes a method of visual inspection

using a Fuzzy Pattern Recognition algorithm. The

method can detect defects such as porosity, lack of

penetration and lack of material in the weld bead. If

the defect exceeds the established minimum standard,

the system will automatically send an alarm and mark

the position where defects appear to help workers re-

pair them. The system is efﬁcient since the speed of

detection can reach 3-4 frames per second. It was re-

ported that the method identiﬁed 65 of the 66 defects

included in the test set.

Computer Vision System for Weld Bead Analysis

403

As described in the works above, the acquisition

and processing of images of the online weld bead be-

comes much more difﬁcult due to environmental con-

ditions. Because of this, we often end up missing

important information from the weld bead image, so

in this research we opted for ofﬂine analysis. Bet-

ween the techniques studied for weld bead discon-

tinuity analysis, the (Schreiber et al., 2009) method

and (Sun et al., 2005) method had a great contribu-

tion to our research since we followed the same trai-

ning and veriﬁcation behavior as we can see in the

next section.

3 METHODOLOGY

The edge analysis is based on the the empty groove

characteristics. Through the proposed method it is

possible to detect the discontinuities shown on Figure

2 wich are misalignments along the weld bead, angu-

lar misalignments, excessive and insufﬁcient material

deposition.

Firstly, images were taken from several weld seam

which qualities vary between acceptable or not. They

were taken using a camera with an passive illumina-

tion system (green leds) which is perpendicularly at-

tached to the proof body. The distance between them

is 15 cm providing a top view of the interest area.

In conclusion, a computer visual system is propo-

sed in order not only to highlight the weld bead edges,

but also to analyze its geometry and detect some dis-

continuities. Furthermore, through a combination of

existent techniques such as PCA (Principal Compo-

nent Analysis) to assist the data reduction, Gaussian

mixture as a probabilistic method and HMM (Hidden

Markov Model) as a statistical model, a edge detection

system is composed. The method execution is illus-

trated on Figure 3. In addition, the MATLAB (Matrix

Laboratory) software was used to develop the system

due to its facility for parameters testing.

The method can be divided on two stages: trai-

ning and testing. In the training stage several weld

bead images are presented in order to make the sy-

stem learn to identify not only good quality edges, but

also discontinuities. The testing stage consists on the

pos-weld veriﬁcation on wich the weld bead edges are

highlighted and the metrics, present in the section 4,

are applied. Through this stage it is possible to assist

not only the welder, but also the inspector to identify

discontinuities that may cause the weld bead invalida-

tion.

3.1 Training

This stage encompasses the image capture of different

weld processes performed by the Bug-O Matic Wea-

ver robot with not only acceptable quality but also dis-

continuities.The obtained images are used as ground-

thruth and its edges are manually marked.

At a ﬁrst moment, the edges of three weld bead

images of 3288 pixels height by 4608 pixels wide

were highlighted. Thus we have a total of 19728

annotated lines since each image has a 3288 pixels

height and two annotated edges. Therefore, each

image provides 6576 samples and 19728 edge points

samples, called edges proﬁle, since each annotated

point is located on a single line.

For each point along the edge an edge proﬁle is

extracted consisting on β pixels on the right and β

pixels on the left of the annoted edge. The edge pro-

ﬁle represents each pixel intensity and its concept is

exempliﬁed on Figure 4. After extraction of all edge

proﬁles, there is a set of vectors that will be used to

learn the edge model. Thus, β = 80 is used.

Applying the PCA technique in the set of edge

proﬁles possible overlaps are reduced thus obtaining

a reduction in the data without signiﬁcant loss of in-

formation. It is used 85 % of the total variance of

the analyzed components resulting on a PCA size of

nineteen.

The PCA space edge proﬁles projections are used

to train a gaussian mixture. The equation 1 represents

the probability of a given projection being an edge.

P(x|θ) =

∑

i=1

g(x|µ

, Σ

), (1)

Where w

= 1, 2,...,k are the k components wheig-

hts, µ

represents an D-dimensional mean vector and

is a covariance matrix.

The Gaussian mixture parameters are learned by a

Maximum Likelihood Algorithm (Expectation Max-

imization - EM) which is a parameter estimation

technique that allow to deal with missing data. Furt-

hermore, the number of Gaussians k was set to seven

since the BIC (Bayesian Information Criterion) pre-

sents a graphical stable behavior at this value when a

1-30 range is analyzed.

It is emphasized that these images are used only in

the training, the test stage makes use of new images

of other weld processes performed by the same robot.

3.2 Tests

After training the proposed method, the testing stage

is performed. To evaluate the proposed method, the

detection of the weld beads edges are oerformed in

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

404

Figure 2: Weld beads geometry.

Application PCA

Application HMM

Extraction Edge ProfilesPictures

Images with the

highlighted edges

Gaussian Mixture

Figure 3: The proposed method block diagram.

β=80 β=80

Figure 4: Right edge image proﬁle graphically represented.

different images that were not used to train the pro-

posed method. For this purpose, the HMM techni-

que is used since it is a statistical model on which the

modeling system is a Markov process with unknow

parameters. Furthermore, it provides the hidden pa-

rameters knowledge based on the observable ones.

The model extracted parameters can be used to rea-

lize new analysis such as pattern recognition (Anzai,

2012) which is the main purpose of its implementa-

tion.

For each given image, a region of interest (ROI)

is determined cosidering 250 pixels for each side of

the empty groove position. It assists to calculate the

probability of a pixel being an edge or not.

Furthermore, the edge position along the image li-

nes follows a Markov model. It means that given the

Computer Vision System for Weld Bead Analysis

405

edge position at line t − 1 the probability of its posi-

tion at line t, p(y

t−1

) is obtained. The transition

models are deﬁned by the equations:

p(y

= y

t−1

) = 0, 9 (2)

p(y

= y

t−1

− 1) = 0, 05 (3)

p(y

= y

t−1

+ 1) = 0, 05 (4)

In conclusion, the appearance model p(x|y) con-

sists in estimating the probability of the variable y

being an edge x. Thus, based on the observable data

(edge proﬁle of the weld bead images deﬁned as x) the

hidden variables (edge position along the weld bead)

are estimated. It means the HMM is responsable for

inferring the edge proﬁle location on the new weld

bead image.

By means of Viterbi algorithm, edge position se-

quence estimation is realized. Moreover, it is neces-

sary to identify the next states on a excellent way jus-

tifying the implementation of the Viterbi algorithm

since it is a optimal search algorithm as can be seen

on Equation 5.

∗

= arg

maxp(y|x) (5)

After this stage, tests are performed on new ima-

ges from the testing set. The region of interest is se-

lected based on the empty groove position. It allows

to analyze smaller areas of the image enhancing the

time aspect of its processing. Finally, the probability

of each pixel within the ROI being an edge is analy-

zed.

4 EVALUATION METRICS

The proposed metrics aim to identify some discon-

tinuities shown on Figure 2 such as lack or excess

of weld deposition and misalignments along the weld

bead. The applied metrics are: minimum, maximum

and average distance between the edges and the empty

groove position, weld bead standard deviation along

the edge using the sliding window method and an-

gle between the edge and the empty groove using the

same method. It is woth mentioning the sliding win-

dow method consists on analyzing adjustable sized

portions of the ROI (window) along its height.

Through the method developed by (Steffens et al.,

2016) the empty groove lines are captured for later

comparison with the ﬁlled one. The mentioned aut-

hor uses the LSD (Line Segment Detector)algorithm

to correctly ﬁnd the empty groove lines since it shows

major performance to other methods in detecting li-

nes in metal sheets. Furthermore, the proposed al-

gorithm realizes the lines ﬁltering only keeping the

vertical ones. The weld parameters, which encompas-

ses electric current and voltage, wire feed rate, robot

linear velocity and oscillation type and velocity, are

automatically determined after extracting the empty

groove lines. Hereafter, the FCAW welding process is

initiated by the Bug-O Matic Weaver robot requiring

four steps for the groove complete ﬁlling.

After the welding process is ﬁnished, the weld

bead is imaged and, ﬁnally, the proposed method pro-

vides the weld bead edges detection. Soon after, geo-

metric evaluation visual metrics are implemented for

discontinuities veriﬁcation.

4.1 Minimum Value

The ﬁrst implemented metric, as can be seen on Equa-

tion 6, consists on the minimum distance between the

weld bead edge and the empty groove line for both

sides of the weld bead. Through this metric it is pos-

sible to identify regions with lack of material deposi-

tion.

= min |Y

−C

| (6)

Where i = 1...n represents the analyzed image

line, Y

refers to the edge position on the image and

is the groove position.

4.2 Maximum Value

The considered metric consists on the maximum dis-

tance between the weld bead edge and the empty

groove line for both sides of the weld bead as shown

in Equation 7. This metric allows the identiﬁcation of

regions with excessive material deposition.

= max|Y

−C

| (7)

Where i = 1...n represents the analyzed image

line, Y

refers to the edge position on the image and

is the groove position.

4.3 Average Value

This metric consists on the average distance between

the weld bead edge and the empty groove line for both

sides of the weld bead as shown in Equation 8. This

metric assists the analysis of the weld bead regularity

since a pattern without considerable oscillations is de-

sired.

∑

i=1

−C

(8)

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

406

Where i = 1...n represents the analyzed image

line, Y

refers to the edge position on the image and

is the groove position.

4.4 Standard Deviation using sliding

window

This metric consists on calculationg the standard de-

viation using the sliding window method with size w

as shown in equation 9. Its application assists the

identiﬁcation of misalignments points along the weld

bead edge.

(k) =

∑

k+w−1

i=k

−C

−

∑

k+w−1

j=k

−C

)

(9)

Where n represents the image lines quantity, w is

the window size, k = {1...n −w}, Y

refers to the edge

position on the image and C

is the groove position.

4.5 Angle between Empty Groove and

Weld Bead Edge using sliding

window

Aiming to obtain the angle between the empty groove

and the edge for both sides the weld bead, it is used

a linear regression method. This way, both the linear

and angular coefﬁcients of the groove and the edge

approximate lines are obtained.Thus, the Equation 10

allows to calculate the desired angle. Through this

metric it is possible to identify if the weld bead exhi-

bits angular misalignments with respect to the empty

groove.

= arctan|

− a

1 + a

| (10)

Where a

is the angular coefﬁcient of the empty

groove approximated line and a

is the angular coef-

ﬁcient of the weld bead approximated line both with

respect to the analized window.

5 RESULTS

This section is divided in two parts. Firstly, the weld

bead edge identiﬁcation method results are presented.

Secondly, the results of the evaluation metrics appli-

cation, as described on section 4, are discussed.

5.1 Proposed Method

In this section some images with highlighted edges

are presented. It is worth mentioning the highlighted

edges were identiﬁed by the proposed method. Thus,

at the left side of Figure 5, the original weld bead

image is shown. At its center, the manually highlig-

hted edges and, ﬁnally, at the right side of the image,

the results of the proposed metod are shown.

a) b) c)

Figure 5: a) Weld bead Image. b) Manually highlighted

edges. c) Proposed method highlighted edges.

Based on Figure 5 the mean error between the ma-

nually highlighted edges and the edges highlighted by

the method is calculated. Thereby, the resultant ab-

solute mean error is 2,9675 pixels for the right edge

and 6,0727 pixels for the left one, as can be seen in

the graphs shown in the Figures 6esquerda. Consi-

dering, based on the camera calibration, that 1 milli-

meter (mm) equals 46 pixels the resulting errors are

0,0645 mm and 0,1320 mm for the right and the left

edges respectively. It is notable the diference between

the ground truth and the proposed method result is mi-

nimal.

Figure 6: Graph of the difference between the manually an-

notated right edge and those highlighted by the method.

Computer Vision System for Weld Bead Analysis

407

Figure 7: Graph of the difference between the manually an-

notated left edge and those highlighted by the method.

Lastly, the table 1 demonstrates the resultant ab-

solute mean error in pixels (px) for the right and left

sides of all tested grooves.

Table 1: Absolute average error between the manually an-

notated border and the border found by the method.

Welding Seams Right Edge Left Edge

(px) (px)

1 2,9675 6,0727

2 5,1442 6,0073

3 6,4234 5,9605

4 4,4349 6,7628

5 3,5703 6,2743

6 5,8406 6,5693

7 8,9605 10,9635

8 4,5493 6,1439

9 8,2597 7,0973

5.2 Evaluation Metrics Results

In this section some evaluation metrics results are de-

monstrated. The results presented below were imple-

mented using the image shown on Figure 5.

The ﬁrst two metrics presented, minimum and

maximum values, are able to identify points on which

insufﬁcient or excessive fusion occurs. According

to American National Standards (ANS) (SOCIETY,

2001), there is a major worry about lack of fusion,

since if the groove is not fully ﬁlled, the weld can be

invalidated or may need another weld pass.

On Figure 8 it is possible to visualize the right

weld bead edge on which there is a possible lack of

fusion. It indicates the weld bead edge (highlighted in

red) is too near of the empty groove line (represented

by a blue line). It should be noted the parameters of

minimum and maximum values may be modiﬁed by

the weld inspector since the mentioned norm only

stablishes the groove needs to be fully ﬁlled not spe-

cifying any minimum or maximum value.

Figure 8: Results of the Minimum value metric.

Through the metric that uses the standard devia-

tion with sliding window it is possible to graphically

identify, through the peaks plotted on Figure 9, where

a weld bead discontinuity may happen.

Figure 9: Graphic of the left and right weld bead edges with

the standard deviation metric using sliding window.

Using the metric of the angle between the empty

groove and the weld bead edge using sliding win-

dow, it is possible to identify if the weld bead exhibits

an angular misalignment with respect to the empty

groove. Furthermore, it is possible to visually iden-

tify the regions on which there is a greater disparity

in angle. On Figure 10 it is possible to see not only

these regions, but also the graph of the mentioned an-

gle.

VISAPP 2018 - International Conference on Computer Vision Theory and Applications

408

Figure 10: Graphic of the angle between the empty groove

and the weld bead edge using sliding window for the left

and right sides of the weld bead.

6 CONCLUSIONS

The gains provided by the welding process automa-

tion in the industry are plenty since it is a insalubri-

ous activity for the welder who stays in contact with

sparks, smoke and eletricity. Therefore, it is increa-

singly common to use robots in this type of task since

it guarantees the ﬁnal process quality, increases pro-

ductivity and reduces the human intervention.

Through a computer visual system attached to the

linear welding robot, the presented work proposes

a weld bead edge identiﬁcation method and metrics

to identify some discontinuities based on the weld

bead geometry. The proposed method successfully

identiﬁed the weld bead edges with maximum errors

of 10.96 pixels. Furthermore, through the perfor-

med tests it is possible to conclude the proposed me-

trics contribute to identify geometric failures that may

compromise the welding process.

REFERENCES

Abendi (2014). Guia end de inspec¸

oes. http://

www.abendi.org.br/abendi/.

Andreucci, R. (2013). Aspectos industriais: Protec¸

ao ra-

diol

ogica. Associac¸

ao Brasileira de Ensaios N

ao De-

strutivos (ABENDI).

Anzai, Y. (2012). Pattern recognition and machine lear-

ning. Elsevier.

Broering, C. E. et al. (2005). Desenvolvimento de sistemas

para automac¸

ao da soldagem e corte t

ermico.

Carvalho, R. S. et al. (2009). Rob

o cnc para a automac¸

da soldagem mig/mag em posic¸

oes e situac¸

oes de ex-

trema diﬁculdade.

Cook, G., Barnett, R., Andersen, K., Springﬁeld, J., and

Strauss, A. (1995). Automated visual inspection and

interpretation system for weld quality evaluation. In-

dustry Applications Conference, 1995. Thirtieth IAS

Annual Meeting, IAS’95., Conference Record of the

1995 IEEE, 2:1809–1816.

Felisberto, M. K. (2010). T

ecnicas autom

aticas para

detecc¸

ao de cord

oes de solda e defeitos de soldagem

em imagens radiogr

aﬁcas industriais.

Hou, X. and Liu, H. (2012). Welding image edge detection

and identiﬁcation research based on canny operator.

In Computer Science & Service System (CSSS), 2012

International Conference on, pages 250–253. IEEE.

Kumar, G. S., Natarajan, U., and Ananthan, S. (2012). Vi-

sion inspection system for the identiﬁcation and clas-

siﬁcation of defects in mig welding joints. The Inter-

national Journal of Advanced Manufacturing Techno-

logy, 61(9-12):923–933.

Kumar, G. S., Natarajan, U., Veerarajan, T., and Ananthan,

S. (2014). Quality level assessment for imperfections

in gmaw. Welding Journal, 93(3):85–97.

Lima, E., Castro, C. A. C., Bracarense, A. Q., and Campo,

M. (2005). Determinac¸

ao da relac¸

ao entre par

ametros

de soldagem, largura da poc¸a e aspecto do cord

ao de

solda utilizando c

amera de alta velocidade. soldagem

Inspection-Out/Dez, pages 182–189.

Machado, E. (2015). Inﬂu

encia dos ventos sobre a quali-

dade das soldas realizadas em estaleiros pelo processo

arame tubular.

Schreiber, D., Cambrini, L., Biber, J., and Sardy, B.

(2009). Online visual quality inspection for weld se-

ams. The International Journal of Advanced Manu-

facturing Technology, 42(5-6):497–504.

SOCIETY, A. W. (2001). American welding society. aws

3.0: Standard welding terms and deﬁnition.

Steffens, C. R., Leonardo, B. Q., d. S. Filho, S. C., H

utner,

V., d. Rosa, V. S., and d. C. Botelho, S. S. (2016). Wel-

ding groove mapping – implementation and evalua-

tion of image processing algorithms on shiny surfaces.

In VISAPP 2016 11th Joint Conference on Computer

Vision, Imaging and Computer Graphics Theory and

Applications, volume 3, pages 265–272.

Sun, Y., Bai, P., Sun, H.-y., and Zhou, P. (2005). Real-time

automatic detection of weld defects in steel pipe. NDT

& E International, 38(7):522–528.

Computer Vision System for Weld Bead Analysis

409