IPA95 SPECIA L SECTION

Check! a generic and specific industrial inspection tool

E.C.0i Mauro T.F. Cootes G.J.Page C.B. Jackson

Indexing terms: Inspection, Shape and grey level models, Object oriented design, Graphical user interface

I Abstract: The authors present an overview of an ‘inspection and control’ package to perform automatic quality control of industrial components, specifically electronic circuit boards. This application has grown out of a set of tools designed initially to aid the development of point distribution models (PDMs) and grey-level models (GLMs) pioneered at the Wolfson Image Analysis Unit in Manchester. These models are both generic and specific. Generic, because they can be applied to most image classification problems; specific, because they aim at the full interpretation of the variability of the objects to be modelled. The underlying vision processing techniques are based on statistical pattern matching. Two synergetic approaches have been followed. The system is trained to recognise the components from either their shape or their grey level appearance or both.

I

Introduction

I. I Problem In the increasingly competitive world market, industry has a rising interest in quality assurance. Industrial inspection by means of CCD cameras is a powerful quality control tool, because it is both flexible and non- invasive. Past inspection systems, however, tended to include specific knowledge of the inspection task, restricting their use and scope, and reducing portability [9]. In addition, a vision expert was required to recon- figure the system after a production change. Sometimes specific inspection problems still require tailored algo- rithms and vision hardware, but a large number of real-life problems can be stated simply in terms of object appearance [lo], defined by object location, dimensional measurements and surface quality, and require only general purpose equipment, with a mini- mum of adaptation to the production line. Ideally, the inspection task should be fully automated and the role 0 IEE, 1996 IEE Proceedings online no. 19960692 Paper first received 22nd December 1995 and in revised form 24th May 1996 The authors are with the Wolfson Image Analysis Unit, Department of Medical Biophysics, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9FT, UK

of the vision expert would be to install the system and provide operator training. The task of the quality oper- ator would be to specify the quality control require- ments (in terms of the desired object’s appearance) by training the system on valid real-life objects. This approach to quality assurance leads to reliable, adapta- ble and reusable vision inspection systems and is suita- ble for total quality management (TQM), which favours standardised quality control procedures.

1.2 Location and identification The challenge in industrial applications is mainly con- cerned with object location and identification, that is the recognition and classification of the object by its appearance and its position in a larger assembly or in a frame of reference. However, objects of the same class are often not identical in shape, or, conversely, an object does not present the same appearance when viewed from different angles or under varying lighting conditions. Deformable models are required, therefore, because they allow variability in the appearance of the imaged objects. A variable model, however, raises the issues of generality and specificity. Generality assures that a deformable shape model is sufficiently general to fit to valid, unseen examples of the object(,) it repre- sents. Specificity assures that the model cannot deform to fit to invalid shapes. In addition, the models should be parsimonious, by condensing all the allowed varia- bility of the object into a small number of shape parameters. Deformable models have clear advantages over fixed templates. A comparison of these two is given in [l 11.

We have, at present, two kinds of models to describe appearance: point distribution models (PDMs) and grey-level models (GLMs). PDMs describe shape varia- tion due to intrinsic variability of the object, or because of different pose. GLM’s describe grey-level variations due to reflectivity changes caused by lighting and the object’s surface texture. Both these models are described in Section 2 (models).

1.3 Solution This paper aims to show how the required general but specific inspection system can be devised. The state-of- the-art image analysis techniques (based on statistical models) which it incorporates succeed in coping with variations in both shape and reflectivity appearance of products on a production line in order to improve acceptheject rates. The set of statistical techniques used in the package stems from a major effort by the Wolf-

24 1 IEE Proc.-Vis. Image Signal Process., Vol. 143, No. 4, August 1996

son Image Analysis Unit over many years [l-191. Shape models are used to achieve robust interpretation of complex images, such as manufactured assemblies. Some aspects of an image can be invariant, whereas others will be subjected to variability. If a set of control points (landmarks) are placed at critical features in the image of the objects and their position recorded for a number of instances of the same object, a flexible tem- plate model can be built for each object. The object’s variability, expressed as the statistical distribution of its feature points, is condensed into a set of parameters. The average shape is then computed for the object. Each parameter represents a degree-of-freedom of vari- ability from the mean shape. Normally, 95% to 99.5% variability can be explained by a reduced set of modes of variation. The residuals fall within the effect of noise, image quantisation and user errors. By a linear (and recently also nonlinear) combination of all or part of the modes, any arbitrary, legal object shape can be reconstructed. Object classification and recognition is achieved by assessing how well a new instance can be represented by the model for each class. A multidimen- sional probability density function is associated with each class, which spans the entire allowed variability for each object. If the new instance falls within the class space then it is recognised positively, otherwise it is rejected. A statistical measure of similarity is pro- vided which allows the quantification of the level of classification confidence. The above framework can also be applied to the grey-level appearance of an object, but instead of using position co-ordinates, the instances of the quantised reflectivity of the object are used to build the model. A grey-level mean and modes of variation are then calculated and used to span the grey-level class space. The synergetic application of both shape and grey-level models to the same objects achieves more reliable classification results [8].

1.4 Problem domain Great care has to be exercised in the application of an inspection system to industry. Problems may arise which are unforeseeable at a research institute. For instance, just-in-time (JIT) strategy requires that com- ponents for a board be available only at the time of demand by the production system. In such a case, except for CAD data, the components’ data for a board might not be available a priori for training the system. This means that the training time has to be reduced to an absolute minimum in order not to affect throughput. In addition, depending upon market behaviour, production may be performed in batches, with slightly different components. For instance, a new supplier might produce components which are different in appearance but electrically equivalent to the previ- ous supplier’s. At times, CAD data might be insuffi- cient for the inspection task because it is designed to aid installation and assembly manufacturing rather than inspection. All these real-life circumstances have to be dealt with by a successful inspection tool. A solu- tion to these problems is a statistically based strategy, which aims at ‘graceful start-up, transition and degra- dation’ of the inspection system. This would ensure a smooth transition between component models and pro- vide confidence measures to managers and operators.

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2 Models

2. I Point distribution models In a PDM, objects are defined by landmark points which are placed in the same way on each of a set of examples. A statistical analysis yields estimates for the mean shape and the main modes of variation for the class. Each mode changes the shape by moving the landmarks along straight lines (linear PDMs) or curves (nonlinear PDMs) passing through their mean posi- tions. New shapes are created by modifying the mean shape with weighted sums of the modes. For some classes of object, linear models are not sufficiently spe- cific or parsimonious (they can adopt implausible shapes, very different from those in the training set, and have more modes of variation than the true number of degrees of freedom needed to describe the objects’ variation). In such cases, nonlinear PDMs are used, because they allow the landmark points to move along curves. There are various kinds of nonlinear models. The polynomial regression PDMs (PRPDM) described by Sozou et al. [12] allow the landmark points to move along polynomial paths. The Hybrid models by Heap and Hogg [22] use a combination of Cartesian co-ordinates and polar co-ordinates for the description of the relative rotation of subparts of a PDM. (i.e. a wheel around its hub). Recently, Sozou et al. have introduced the multilayer perceptron PDMs as a means to carry out generalised nonlinear principal component analysis to tackle variations which do not follow polynomial paths [13]. Finally, flexible 3D mod- els from uncalibrated cameras [14] prove valuable in the inspection of objects which vary in three dimen- sions, but of which only a set of two-dimensional images is available. It is our intention to implement these latest nonlinear models in Check to make it more generic and able to cope with extrinsic (e.g. pose) as well as intrinsic object variability (see Section 7).

2.2 Theory of point distribution models A shape in a 2-D image may be represented by the position x of a set of n landmark points [x = (xl, yl ... x,, y,)], and is thus described by a point in 2n-dimen- sional landmark space. Examples of shapes belonging to the same class will create a distribution of points in landmark space.

The linear PDM is created by carrying out a simple principal component analysis (PCA) of this distribu- tion. The t eigenvectors corresponding to the largest t eigenvalues of the covariance of x give a set of basis vectors for a flexible model. A new example is gener- ated by adding to the mean shape a superposition of these basis vectors, weighted by a set of t shape param- eters (bl, b2, ... bt).

x = F + P b (1) Where: X is the mean shape

b is a vector of shape parameters P is matrix of basis eigenvectors

We call the basis vectors: the modes of variation of the shape.

The principal component analysis can be thought of as performing two statistical functions. Firstly, by retaining only the first few eigenvectors, which explain most of the variance (e.g. 99%), it enables a parsimoni- ous model with a small number of shape parameters to be created. Thus it performs linear dimensionality

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modelling tool Plant shape

models

image board mode‘s

(on t he product ion

I I ne)

models - appearance *

quality control report

Fig. 1 Logical feedback diagram of industrial inspection tool

reduction. Secondly, the distribution of the training examples is linearly independent in the t eigenvectors which are retained, giving the most specific (i.e. the most compact) model possible for a linear transforma- tion of landmark space.

2.3 Grey-level models GLMs are used when the objects of interest exhibit lit- tle variation in shape and size, such as capacitors in printed circuit boards (PCB), but vary considerably in grey-level appearance. Simple correlation techniques perform poorly, because variations in appearance, caused by lighting changes and camera noise, can easily confuse standard correlation methods used for locating the components. GLMs are based on a statistical description of the variation of reflectivity appearance of the objects. When a valid model of the typical (mean) appearance of the object is combined with a set of modes of allowed variability, then any possible appearance of the object can be recreated. True and false examples can be discriminated by a statistical fit- ness value. This allows the operator to estimate a threshold which will fail no more than a given number of correct components.

2.4 Theory of grey-level models These statistical models are derived from a set of train- ing examples. The examples are rectangular regions of image which contain instances of the component under scrutiny. Each region (’patch’) is then sampled in x and y at regular pixel intervals and a vector x is built with all the values of grey-levels in the patch. By repeating this process for a number n of times, a linear eigen- model can be built. By applying principal component analysis the mean X and a set t of principal modes of variation are obtained. These modes are represented by the n x t matrix of eigenvectors P. Following this tech- nique, a patch x can be approximated by:

(2) The difference between the patch and its approxima- tion is the residual error r given by:

After the model is built, it is then tested upon new instances in order to determine the fitness threshold. The most sensible estimate is based upon the estima- tion of the variance V, of the sum of the squares of the residuals. This can be deduced from ‘miss-N-out’ exper- iments using the training data [15]. It is therefore possi- ble to estimate vj, the variance of rj, the residual for the

x 2 x’ = X + Pb where b = PT(x - X)

r = x - - X ( 3 )

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jth element of x across the training set. The smaller the variance the better each j element is modelled. The fit- ness measure f which stems from this analysis can then be expressed as follows:

j=n -2

f = M t + x 2 U:, (4) j=1

where Mt is a measure of the distance from the mean in the model’s dimensional space. The statistical model of the data, given by eqn. 2, then becomes:

which incorporates a measure of the residuals. More information on the model building procedures is con- tained in Cootes et al. [7].

x = % + P b + r (5)

3 System description

3. I System structure The proposed inspection system is composed of four parts (Fig. 1): (i) Check, an overall inspection and control program (ii) Indexer, a program that communicates with the production line and acquires the images (iii) Plant, a program which trains point-based shapes for later recognition (iv) Patch, a program which trains the system with grey-level ‘patches’ for later appearance recognition. These four parts are window-based menu driven graph- ical user interfaces (GUI) and communicate with each other via disk files. The main program in the suite is Check (Fig. 2). The three other parts provide services for this program. Fig. 1 shows a logical feedback dia- gram of how the system works. The input to the system is the board under inspection. Its mosaic composite image is captured by an array of CCD area scan cam- eras, providing the input for the inspection and control tool. From it the user can invoke Plant (the shape modelling tool) and Patch (the grey-level modelling tool), which appear in the two main ‘feedback loops’. These tools ‘interact’ with each raw image, during ini- tial training and subsequent normal operation, and aim at building a combined specific model of each compo- nent in the board image. Check then uses the models so constructed to ‘check’ them against stored measure- ments. This feature is akin to computer aided design (CAD). The inspection system has structures such as libraries, library components, assemblies and assembly

243

Fig.2 Image of s

:he( ectit

:k, 3n

main of PC

window :B board is loaded and is readv for training bv ouerator note the electrolytic

capgcitor at centre. The white mark on the rim sho& i t s polarity. ’ I

Fig.3 Plant, main window Example of a resistor (R33) is marked up for recognition

components, which are concepts familiar to CAD trained personnel.

Plant (Fig. 3) is a user-friendly interactive package designed to build point distribution models of the objects under inspection. It can work standalone or be launched automatically from Check. It allows the user to ‘plant’ and manipulate points and lines on the cho- sen image. Statistical models of allowed pattern varia- bility can be created from these sets of points. These models are used in the classification task. Classification allows the system to distinguish between different fami- lies of components and different components within a

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Fig.4 Patch, main window Grey-level ‘patch’ of electrolytic capacitor is under training within the white rectangular box.

family, and to recognise damaged/misplaced compo- nents.

Patch (Fig. 4) is a program designed to assist the user in configuring and training grey-level appearance models. Patch allows the definition of rectangular regions (patches) covering each component. A training set consists of patches from similar components. Inter-

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action is provided to allow the user to move the patch to any position or orientation in the image. To assist in training, the best match in an area of image can be found, thus once a single example has been trained the user can simply find or add examples to the training set semiautomatically. By applying Patch to each new image, models of patches are used to locate and classify each target component in a simple and elegant way. The model can be further refined by an iterative proc- ess. Each training example can be matched against the existing model and adjustments made to the exact loca- tion of these training examples. This process removes the ‘history’ inherent in models where the earlier exam- ples were trained by hand or with models trained from a small number of examples. Finally, Patch, like Plant can be launched from Check or used standalone.

The assembly libraries contain the description of each component’s features and the dimensional and rotational information of its position in the electronic board.

The transforms specify the image viewpoint (i.e. cam- era calibration parameters) to allow for accurate meas- urements.

The output of the monitoring system is represented by the quality control reports, which quantify the qual- ity of the product. From the cybernetic point of view, this inspection tool acts as a transducer, converting board images into a set of product quality values.

3.2 System features

3.2. I Optics The proposed inspection system comprises an array of calibrated cameras. The mosaic view of the PCB under inspection is produced by focusing each camera onto a specified and contiguous section of PCB. The system supports 8 cameras as standard, or 16 as optional. The field of view (FOV) is defined by the user. The higher the magnification, the smaller the FOV and the smaller the component that can be detected, and, therefore, the higher the accuracy and repeatability of measurements. In a typical configuration, the FOV of each camera in the array is set at 76.8mm by 57.5mm. In this configu- ration, 1 pixel is equivalent to 0.1 mm2 on the PCB.

3.2.2 Measurements; All the measurements of position and orientation are reported in real-world units (mm or degrees) relative to the PCB co-ordinate system. The measurement repeatability of the compo- nent position is ? 0.1pixel (or ? O.Olmm at the above FOV). The measurement accuracy is f 0.2pixels (or 20.02 at the above FOV). The orientation measure- ment repeatability is 2 0.5degrees. The orientation measurement accuracy is f Idegree. The tolerances for component position and orientation are defincd by the user directly. The user also can define the search areas within which the components are to be detected.

3.2.3 Operational speed: The grey-level models achieve a detection speed of 10 surface-mount devices (SMD) components per second. The point distribution models, mainly used €or leaded devices, achieve a detection rate of two components per second. The above times are dependent on the size of the search areas defined and the specification of the computer hardware. In our case, the timings refer to 28 MIP, 4.8 MFLOP machines. Faster performances are available with faster machines or multiprocessor configurations.

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The component libraries are supplied with the sys- tem. However, the user can also define his own librar- ies, with disc space being the only constraint on their number or size. The user has the facility to define the PCB schedules, in any number allowed by the disc space, and the quality reports.

4 Industrial application approach

Check has been developed for the Planet project (EUREKA EU265), a European collaboration between the University of Manchester (UK), Marelli Autronica (FranceAtaly), AMT (Eire) and Jaeger Ib (Spain). Its main task is the automatic inspection of electronic con- trol unit (ECU) printed circuit boards (PCB) to be mounted inside vehicles.

The planning of an industrial application of Check such as this, can be subdivided as follows: (a) Determine the production areas where image inspection can be most successful or where other inspection tools fail. An example of this is the inspec- tion of electrolytic capacitors (Figs. 2, 4 and 6), which have a definite polarity. If the positioning in a board is inverted, then the capacitors will be destroyed after a short while in active use. This fault is impossible to detect electrically (b) Measure the throughput at that stage, and time the inspection operations in order to verify if any transport delay occurs

Fig. 5 Zoomed-in portion of PCB bourd with number of resistors infield of view Each one of them is used to build resistor PDM model, training shape for resistor R33 is shown as a white polygon superimposed onto the component’s image

Fig.6 Four example images ofthe ele, tor White box shows portion of image used to b. ferent appearance of component in the four irLLages

;vel model, note the dif-

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(c) Compare with other forms of testing (if any) (d) Estimate the model-training constraints

Fig.7 Seven valid examples of the surface-mount component in their bounding boxes

(e) Select inspection personnel who are initially respon- sible for training the system for each target component and for the monitoring of the pasdfail figures of merit produced by the system a Study the variability of the components to measure the performance of the models (g) Install the image-grabbing and computer system on the company’s premises (h) Pass the end-user acceptance criteria (i) Set up on-call and remote maintenance and project development to implement novel, state-of-the-art mod- elling algorithms

5

Check has been used for the inspection of electronic board components in a production line. Tests, which were conducted in situ at the manufacturer’s premises showed the potential of the proposed inspection tool. To provide the reader with a flavour of the issues in industrial inspection we include an example of a model building procedure using Check: (i) Acquire a series of images containing the features that have to be detected. (ii) Create a series of hierarchical libraries of compo- nents that will be used to store models or other training data. (iii) Using a subset of images from step 1, train the model for each component. Shape models and grey- level models are created at this stage. For example, Figs. 3 and 5 show a training example for a resistor shape model, and Fig. 6 shows some training images

Example of a PCB inspection

Fig. 8 3 ‘insert’ windows show grey-level patch of the component at same, half and quarter sampling levels. This speeds up the search of the component in the region of interest

For each component multi-level grey-level search pyramid is created, by subsampling patch’

246 IEE Proc-Vis. Image Signal Process, Vol. 143, No. 4, August 1996

for an electrolytic capacitor grey-level model (note the difference in illumination and position of the compo- nent). After successful training of the models, portions of the original image data are stored with the models. This allows the users to determine which examples are used in the models by simply changing the position of the examples in the filing system. Storing the image data also allows the models to be refined by iterative retraining. (iv) Once the models have been trained, inspection assemblies are defined. The user can then define the required positions in the image for the model to search. This can be set up either graphically or numerically. The co-ordinate system used can either be in pixel units or real-world units via a simple calibration procedure.

Fig.9 Component is searched in the region of interest andfound System then checks it against mean shape and allowed variability and declares it acceptable (‘pass’)

Fig. 10 Component fails the test because its appearance exceeds the model’s allov variability, The system therefore suggests the operator retrains the models include the current example as a valid instance

Case where threshold is artijicially set very high Jed to

The user also has the choice of defining located objects that specify a frame of reference from which all subse- quent measurements are made. (v) The system is now set in a ‘debug’ state and the remaining images are used to test both the assembly and the models. Errors at this stage can be edited easily and examples added or removed from the models.

(vi) When the assembly is fully defined, it can be installed in production. Full diagnostic information is produced as the system inspects the components. This information can be extracted in the form of graphs to monitor the process under inspection.

(vii) The system is ready for operation. The inspection of PCB is initiated and information is provided to the user. First, the system is trained on valid ‘patches’ and a specific model of a component is built. A number of valid examples is used for this purpose (e.g. seven as in Fig. 7, shown in their bounding boxes). From now on the procedure is automatic, requiring the user to spec- ify only the search area. The search procedure is car- ried out using a multilevel grey-level pyramid algorithm [16], where the search is performed first on subsampled versions of the original image (Fig. 8). This results in a faster search, which is also more robust because it is less affected by local minima. The operator can then inspect unseen instances of the component. Fig. 9 shows the search in the region of interest (ROI) for an instance of the component. The system recognises it so that it is granted a ‘pass’ mark. In Fig. 10, the thresh- old for the fitness method is purposefully set to a very high value. The result is that the component fails the test, because it has not been detected. This has hap- pened, because the model was made extremely specific. In this case, if the component ought to have been detected, then the system suggests that the operator should retrain the model to include the current instance of the object. Fig. 11, instead, shows a situation where the position co-ordinates assigned to the component are wrong. In this case, the component fails the test.

Fig. 11 wrong Component fails the test

Case where position co-ordinates assigned to component ar a

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The advantage of the above approach is that at no time during training, model building and testing the system makes any assumption regarding the validity of the training set or a new instance presented for evalua- tion. It is the user (i.e. the expert in his own process, procedures and hardware), who determines the thresh- olds, the confidence factors and the cost-effectiveness of the quality assurance versus throughput time. For a detailed analysis of object location and identification by grey-level models refer to [7].

6 Degree of innovation

Check is novel because it departs from fixed template matching and achieves classification by coping with a great deal of variability in the object’s shape. In partic- ular, Check bypasses one of the major problems in modern inspection systems, which is the need for high magnification in order to interpret finely resolved images. The problem with high magnification is the need for precise X - Y movements of the scanning camera system over a PCB or vice versa. The need for accurate displacement actuators increases cost and physical system size. In addition, the actuator system is prone to failure or deterioration through ageing. In Check, instead, the inspection optics can be mounted directly onto PCB manufacturing transfer lines. Con- ventional PCB transfer line part handling can then be used to manipulate the boards under the cameras. It is therefore cheap, simple, and easy to install and main- tain.

Finally, Check is also novel because it is designed in an object oriented framework written in C++, which suits the composite nature of assemblies and subassem- blies inspection. This strategy also applies to each mod- elling tool. Both Plant and Patch are ‘objects’, which allow reliable code security, modularity, maintenance and expansion.

7 Future

The models and the graphical interface tools used for Check are a continuing research issue at the Wolfson Unit. At present studies are carried out on: (a) Nonlinear point distribution models [12, 131 to cope with rotational modes of component subassemblies (b) 3-D projective PDMs [14], to cope with changes in point-of-view of the camera, to reduce the need for camera calibration; (c) 2-D projective PDMs, to simplify and speed up the models shown at point (b); (d) automatic image search, model training and point planting [ l l , 16, 17, 211 in 2-D and 3-D, to reduce operator time and human error on system start-up and subsequently on batch transition; (e) Finite element methods 1181, for artificially creating parameter variability when, at start-up, only few instances of a component are available; (0 Grey-level patch tracking [19] . Methods for reducing the size of training sets are cur- rently under investigation with the aim of producing more specific models, which can cope with larger varia- bility and reduce training time. At present, Check is implemented on UNIXB-based work-stations, but a PC version is being developed.

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8 Conclusions

The inspection tool presented in this paper shows that models based on a statistical description of shape and grey-level appearance of objects are now mature for industrial application in a production line. The applica- tion stage has begun to address real-life management and logistic problems. This, in turn, stimulates research to improve speed and reliability. Our main aim in the development of the tool was to remove the need for a vision specialist in the quality control loop. In addition, the quality operator, who is an expert of the product and the company’s production procedure, is now in charge. The operator defines detection levels and acceptireject thresholds. Heishe can decide on the accu- racy and extent of training required and allows TQM managers to set quality goals in a statistical frame- work.

Other applications of this package are envisaged: cash card machines, security devices, transport manage- ment and medical imaging. These fields share similar problems, and the advances in one discipline lead to cross-fertilisation of ideas in the others. The concept behind these techniques is that shape and grey-level appearance are specific to each object in an image, but the tools required to model them can be used in a vari- ety of applications, from industrial inspection to medi- cal imaging, from remote sensing data analysis to new computer-user interfaces.

9 Acknowledgments

ECD and TFC are supported by EPSRC.

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