SMD segmentation for automated PCB recyclingWei Li, Bernhard Esders and Matthias Breier

Institute of Imaging and Computer Vision, RWTH Aachen University, 52056 Aachen, Germanywei.li@lfb.rwth-aachen.de

Abstract—Due to the complex structure and the great variabil-ity of printed circuit boards (PCBs), an accurate analysis of theirmaterial composition and consequently an efficient recycling iscurrently hardly possible. Facing these problems, we proposed anautomated PCB recycling system with a core component for theinformation retrieval. As a first step towards automated recyclingbased on a comprehensive analysis of PCBs, the segmentation ofsurface-mounted devices (SMDs) was presented in this paper.Inspired by the technical background of SMDs, they weredivided into two main groups: 1. small devices, such as resistors,capacitors, and 2. integrated circuits (ICs). Then assembly print-based segmentation and color distribution-based segmentationwere carried out for the two groups respectively. In comparisonto device template-based and solder joint-based approaches, ourapproach is more flexible and more reliable with respect to thevariability of devices and the erosion or soiling of solder joints.As confirmed by the evaluation, satisfying results were obtained.The approach was also evaluated for different imaging conditionsso that improved performance can be achieved using appropriateimaging settings.

Keywords—Segmentation, SMD, PCB recycling, imaging.

I. INTRODUCTION

Modern consumer electronics (CEs) provide diverse usefulfunctionalities and therefore make our life better and morecomfortable. Nowadays, they are becoming probably essentialfor mostly every ones daily life. Due to the relatively shorterlifetime of CEs compared with industrial machines and therapid rise of new generations, used electronic equipment isdiscarded annually in large quantities. Since electronic wastecan contain massively reusable components, valuable materialsand toxic substances, a recycling of it is economically benefi-cial and environment-friendly. To achieve this, several politicalattempts have been made. For example, within the EU, thereis an European Community directive[1] on wasted electricaland electronic equipment (WEEE) available for collection,recycling and recovery of electrical goods.

As the core components for the realization of designedfunctionalities of CEs, PCBs are always to be found in suchelectronic equipment. For an intuitive impression, a demo PCBwith some marked SMDs is illustrated in Fig. 1. Usually,diverse components are mounted on them at high density.Moreover, PCBs are manufactured in versatile shapes andcolors. All of this makes PCBs by far the most complicatedbuilding blocks in electronic products[2]. Technically, it iscurrently very challenging to recover reusable componentsand precious materials from waste PCBs and separate toxicsubstances during recycling.

For the last decades, there have been much research work

SMDs

Fig. 1. Demo PCB with SMDs

on the development of both semi-automated and automatedPCB recycling systems. A comprehensive overview of existingtechnologies and systems was presented by Li et al. in theirstate-of-the-art survey[2] of PCB recycling. In [3] and [4],Knoth et al. introduced new concepts to automate disassem-bling electronic equipment and described the development ofa flexible semi-automatic disassembly cell for PCBs, under thecontext of reuse and material recycling. Contrary to most dis-assembly concepts only adapted for predefined target groups,a modular system composed of flexible cells for disassemblyfamilies was proposed. Disassembly families are those targetobjects requiring nearly the same disassembly operations.Depending on the objects currently to be demanufactured anddetermined by a cell control unit, suitable cells were employedto carry out the corresponding disassembly. However, nodetailed description about the information retrieval, the mostessential part for the success of automated disassembly, waspresented in the papers. In [5], aiming at regaining electroniccomponents for reuse and assuring their quality, Stobbe etal. developed an automated desoldering system consisting ofcommercially available standard components. After the patternand structure-based recognition, all desoldered components areinspected using an automated optical inspection (AOI) system.The process parameters of desoldering were also investigated.It was shown, high quality of recovered components could beachieved using a smart processing.

Even though great progresses were made for the recycling,there are still couple of unsolved problems figured out in[2]. Due to the complex structure and the great variability ofPCBs, an accurate analysis of material composition is barelypossible. As a result of this, most recycling systems recovermerely metal content of scraps. Moreover, as most recyclingprocesses are based on the Knudsen process[6], a competitive

preprocessing PCBs PCB processing

waste collection

WEEE

PCB analysis

process optimization

sensors

recycledmaterials

retrieved information

process configuration

sensor data

Fig. 2. Automated PCB recycling system

resource recovery is unavoidable. And last but not least, thescarce materials used as tuning elements are dispersed onlyin small amount in PCBs. There are hardly any appropriatemethods for efficiently recovering the precious materials.

Facing these problems and in order to achieve an efficient,beneficial and environment-friendly recycling, we propose anautomated PCB recycling system. In Fig. 2, the system isillustrated with several subsystems. The corresponding ma-terial flow and data flow are also sketched out respectively.After the preprocessing of collected WEEE, PCBs are readyfor the information retrieval. To perform a comprehensiveanalysis of PCBs, multi-modal sensors (cameras, 3D scan-ners, energy dispersive spectrometers, etc.) are employed andacquire relevant information. As the most distinctive partof the proposed recycling system, a sophisticated subsystemconsisting of diverse information processing units for PCBanalysis is introduced. A sketch of this subsystem is availablein Fig. 3. Based on the retrieved information, including mate-rial composition, material distribution, properties of materialcarriers (electronic devices and other components), an optimalconfiguration of available recycling processes is generated forthe control of PCB processing. Typical such processes aredisassembling[7][8], shredding, concentration, hydrometallur-gical processing[9] and pyrometallurgical processing[2].

In this paper, as a first step towards an automated PCBrecycling, the segmentation of SMDs is presented. It is basisfor further analysis steps. For instance, the recognition ofsuch devices regarding their characteristic features determinedby feature analysis. Then, with help of a provided database,material composition and properties of corresponding devicescan be retrieved. The remainder of the paper is organized asfollows: necessary technical background of SMDs is summedup in Section II. This gives an inspiration for the proposed

PCB analysis

. . .

sensordata

3D reconstruction

SMD segmentation

spectrum analysis

retrieved information

material information retrieval

information fusion

component & PCB recognition

Fig. 3. PCB analysis for information retrieval

segmentation approaches. In consideration of the two maingroups of SMDs, the small devices and the ICs, details ofimage analysis are described in Section III and IV for thetwo device groups respectively. To illustrate the performance,segmentation results are evaluated and discussed in Section V.Conclusions of this paper are drawn in Section VI.

II. TECHNICAL BACKGROUND

Electronic devices and other components are employedto realize the desired functionalities of PCBs. In order toimplement them on circuit boards, different technologies canbe applied with respect to the properties (size, application,etc.) of used devices and components. For SMDs, they aremounted directly on PCBs using the surface-mount technology(SMT). In contrast to through-hole components (THCs) whichare fixed with wire leads into additionally drilled holes incircuit boards, a higher device density can be achieved forSMDs since they are usually smaller than THCs and a doublesided mounting of such devices on circuit boards is possible.

(a) Resistor 1206 (b) Resistor 0603 (c) Capacitor 1206

(d) Capacitor 0603 (e) SOP IC (f) QFP IC

Fig. 4. Examples for SMDs. The codes 1206 and 0603 available in (a) -(d) address the dimension information of corresponding SMDs. In (e) and (f),the ICs are assembled in SOP and QFP respectively.

Regarding the sizes and packages of SMDs, they can bedivided into two main groups: small devices and ICs. In Fig. 4there are some typical SMDs illustrated as examples. Thecodes 1206 and 0603 available for small devices in (a) - (d)address the dimension information of corresponding SMDs.For instance, 0603 stands for the imperial size of 0.063 in× 0.031 in (length × width) and the metric size of 1.6 mm× 0.8 mm. In (e) and (f), the two ICs are assembled in thesmall out-line package (SOP) and the quad flat package (QFP)respectively. Compared to small devices, ICs are visually withhomogeneous surface.

To increase the throughput of PCB production, automatedassembly was developed to replace the manual operation. Asaid for this automated process, the assembly print is oftenavailable on circuit boards to enable automatic placement ofdevices and also AOI of manufactured PCBs. For SMDs, themost important assembly print is the printed white bordersof devices indicating the desired device placement. For illus-tration, a small section of a demo PCB with SMDs and thecorresponding assembly print is presented in Fig. 5. As shownin the image, there is always a printed white border boundingthe placed SMD. However, unfortunately, not all bordersare perfect for recognition. For instance, VIAs are electrical

Fig. 5. PCB with assembly print. Some printed bounding borders are brokenby VIAs that are marked with red circles. There are also bounding borderswithout any mounted devices but only with solder joints available.

connections between layers in circuit boards that can makethe recognition of borders more difficult. In Fig. 5, red circlesindicate the VIAs breaking bounding borders. Moreover, if adouble sided mounting is applied, bounding borders with onlysolder joints can be found because of the devices mounted onthe back of the circuit boards.

III. SEGMENTATION OF SMALL DEVICES

Despite the large number of research works on PCBrecycling, very few publications have suggested applicableapproaches for device segmentation. Opposite to this, on thesimilar topic device segmentation for AOI, a good deal ofresearch was presented. In [10], Zeng et al. described a solderjoint-based algorithm for locating PCB components. A well-designed LED illumination was employed to generate desiredcolor distribution patterns on the surface of solder joints. Usingthe illumination normalization approach introduced in [11],Mar et al. were able to achieve a segmentation of solder jointsinsensitive to illumination variations. All of these approachesdeveloped for AOI require clear difference between solderjoints and other similar objects, for example, the assemblyprint, which cannot be necessarily satisfied for waste PCBsdue to erosion or soiling.

Inspired by the technical background of SMDs summed upin the last section, an assembly print-guided segmentation ofsmall devices is then considered to replace any solder joints-based approaches. As guidance for mounted small devices,all printed bounding borders are to be recognized. The foundborders and the bounded areas are then initialized candidatesfor segmentation. By means of a hierarchical assessment, non-bounding border objects and bounding borders only with sol-der joints are canceled sequentially. Finally, all small deviceswith their bounding borders are segmented.

A. Candidates for Small Devices

Shadows and Reflections on circuit boards can lead toincorrect recognition of bounding borders due to low con-trast and inhomogeneous illumination. With a preprocessingto emphasize the thin lines of printed device borders, the

Fig. 6. Corner-based model fitting using eight corner templates

image sharpening method unsharp masking described in [12]is applied with the ability of improving local contrast andcompensating inhomogeneity of illumination to some degree.Subsequently, an adaptive thresholding algorithm using theintegral image presented in [13] is employed to detect theborder pixels in preprocessed images. Instead of a globalthreshold for the complete image, local thresholds are com-puted within an averaging window of current pixel position, sothat only the bright pixels in comparison to local neighbors areselected. The integral image is used to make the thresholdingcomputationally more efficient.

So far, all border pixels are detected, as well as thepixels of printed labels and other unexpected artifacts. Asillustrated in Fig. 5, bounding borders are always rectanglesor quasi-rectangles. This geometry information can be usedto determine possible candidates for searched devices. Inconsideration of the size variation and the incomplete orincompletely recognized borders due to VIAs, occlusions andstrong shadows, a corner-based model fitting is applied insteadof the straightforward template matching for the search ofcandidates. The model parameters m = [cT , w, h]T of eachrectangle can be determined using arbitrary two diagonalcorners of four where c is the centroid, w and h are thewidth and the length respectively. This makes the corner-basedapproach more robust in case of only partly available borderpixels. Taking the corner v1 = [xv1, yv1]

T lower left andthe corner v2 = [xv2, yv2]

T upper right for example, m isestimated according to

m =

[I/2−R

]· v1 +

[I/2R

]· v2,

where I =

[1 00 1

],R =

[cosψ sinψ− sinψ cosψ

].

ψ is the rotation angle of the searched rectangle in images andcan be estimated with help of Hough transform algorithm[11].

All together eight corner templates are presented in Fig. 6.For each corner position: upper left (ul, yellow colored), upperright (ur, green colored), lower left (ll, purple colored) andlower right (lr, red colored), perfect and slightly roundedcorner templates are used. Once the morphological operation,erosion[12], has been carried out eight times using the rotatedtemplates with respect to ψ, all left corner pixels are mergedfor each corner position. The merged detection results aregroups of connected pixels. Each group represents only onecorner. Let Vul, Vlr, Vur and Vll denote merged corner pixelsfor positions ul, lr, ur and ll respectively. Rectangles canbe estimated either with pixels from Vul and Vlr or withpixels from Vur and Vll. For this model fitting, random sampleconsensus (RANSAC) algorithm[14] is employed for its robustestimation in case of outliers. The recognized border pixelsbelonging to currently estimated model are used to calculatedthe completeness of the corresponding rectangle. This valueis further normalized with respect to the size of the rectangleand considered as the quality of the fitted model. Let Sand S denote the pixels of a complete rectangular border

(a) RGB image (b) Fitted bounding borders

(c) Valid bounding borders (d) Segmented small devices

Fig. 7. Segmentation of small devices: to segment small devices in a PCBimage illustrated in (a), candidates for bounding borders of searched devicesare determined using corner-based model fitting and are plotted as yellowrectangles in (b). Subsequently, non-border objects are canceled with respectto a series of cancellation criteria and only real bounding borders are stillavailable in (c). Regarding the bounding borders only with solder joints, theyare also removed from the current segmentation. Green rectangles in imagesindicate the final segmentation result.

and the found border pixels of this rectangle respectively,the corresponding normalized completeness Q is defined asQ = #(S)/#(S), where #(·) denotes the cardinality of aset. For each group of the connect corner pixels in Vul, Vlr,Vur and Vll, only the rectangle model with the best qualityis selected as the bounding border of searched candidates forsmall devices. As intermediate result of segmentation, fittedbounding borders on a demo PCB of Fig. 7(a) are plotted asyellow rectangles in Fig. 7(b). Green borders are also plottedto indicate the final segmentation result.

B. Hierarchical Assessment

Obviously, there are too many unexpected candidates inFig. 7(b) besides searched small devices. They are artifactscaused by non-border objects and real bounding borders butwithout any devices. For this reason, a hierarchical assessmentis applied for the cancellation of false candidates.

1) Non-Border Objects: Some non-border objects, suchas printed labels, show structures similar to the corners ofbounding borders. After the corner-based model fitting, plentyof non-existent rectangles are generated. The most straight-forward criterion used for cancelling such false candidatesis the normalized completeness Q of rectangles. It describeshow probable the candidate is a real bounding border. Aslearned in experiments, with a threshold tQ a great part offalse candidates is rejected while no significant affect ontruly searched targets is observable. In other words, the truecandidates are insensitive to the variation of tQ. Some furthercriteria for the cancellation of non-border objects are also

considered. Small devices always appear in certain forms.Referring to a generated gold standard for SMDs, a map ofvalid device sizes can be obtained. The map is then dilatedusing proportional safety margins ±δ% for each valid sizeand therefore turns to be more error-tolerant. Furthermore, forthose virtual candidates from artifacts, it is not necessary tosatisfy the non-overlapping constraint of physically existentdevices. Many of them are overlapping with one another orwith real borders. To remove the multiple detections, non-maximum suppression (NMS)[15] is applied. Regarding thenormalized completeness computed during model fitting, onlythe best rectangles from overlapping candidates are retainedfor further assessment. Another cancellation criterion is basedon the texture analysis of local region. As expected, devicesshould have other texture features rather than the same ofartifacts like stickers and plug-in contacts. To this end, localbinary pattern (LBP)[16] and Haralick features[17] from gray-level co-occurrence matrices (GLCM)[18] are analyzed andused to identify remaining non-border objects. For classifica-tion, support vector machine (SVM) and k-nearest neighborclassifier (KNN)[19] are employed. None of the both classi-fiers provides superior segmentation result in comparison tothe other one. But SVM is preferred for its computationalefficiency during classification.

2) Bounding Borders without Devices: As mentioned, somereal bounding borders are only with solder joints due todouble sided mounting and not occupied by devices. Thesecandidates also have to be removed from current segmen-tation. For an appropriately mounted device, solder bridgesmust not be available between its solder joints. Taking thisinto account, the background (substrate) of the circuit boardmust be visible in the middle of such false candidates.If the background color and the colors of devices can bedetermined and separated from each other, the unoccupiedbounding borders can then also be identified. Unfortunately,the background color and the device colors are sometimesvisually similar. Most standard segmentation algorithms canonly provide non-satisfying results. Ridge-based analysis ofdistributions (RAD) introduced in [20] is a variant of ridge-based segmentation. Benefiting from the analysis of connectedridges in the histogram of color values, distributions of colorsare estimated. As a result, corresponding distributions can bedetermined for different colors even if the colors are withslight differences and the illumination varies a lot over thewhole image. Another advantage of RAD is the implicit fusionof spatially distributed regions with the colors from samedistributions, or in short words: regions of same colors aremerged respectively. Placing water sources at determined ridgepoints and applying the watershed segmentation algorithm[21]on the inverse histogram (the original one multiplied by −1),the surface of PCBs is then separated into regions of samecolors (color distributions). With help of the histogram ofhue values defined in the hue-saturation-value (HSV) colorspace, the background color is selected as the dominate colorvalues in the histogram. All candidates with the determinedbackground color in the center are identified as unoccupied

(a) RGB image (b) RAD segmentation (c) Segmented ICs

Fig. 8. Segmentation of ICs

bounding borders and therefore without searched devices.3) Small Devices: After the hierarchical cancellation of

non-border objects and bounding borders without devices, onlythe searched small devices are retained in the segmentationresult. For the demo PCB in Fig. 7(a), almost all target objectsare successfully segmented and indicated with green rectanglesin Fig. 7(d). For the only target device missed in the imageupper left, its bounding border is partly covered by otherdevices and no diagonal corners can be found for the modelfitting. This is rather a rare occurrence. Despite printed labels,solder joints, stickers and unoccupied bounding borders, nofalse candidate is assigned to the final segmentation.

IV. SEGMENTATION OF ICS

Due to their much larger sizes in comparison to resistors andcapacitors, ICs often cover partly or totally the correspondingassembly print. Therefore, the assembly print-based segmenta-tion is inappropriate for such devices. As mentioned in SectionII, ICs are visually with homogeneous surface. Segmentation isthen possible, if the homogeneous surface can be distinguishedfrom the background of circuit boards.

The RAD-based circuit board segmentation was introducedin last section. For a demo PCB with ICs in Fig. 8(a), thesurface is segmented into background regions and N disjunc-tive non-background regions. All of them are represented inFig. 8(b) as homogeneous regions with different gray values.Regarding the lower boundary tIC of IC sizes in images,all non-background regions with less than tIC pixels areremoved. A further size validation is carried out using distancetransform. The distance value di is assigned to the pixel pi

with pi = [xi, yi]T in a region R and there is

di = argminpj∈B(R)

((pi − pj)T (pi − pj))

12 ,

where B(R) denotes the border pixels of R. Only the regionswith at least M distance values exceeding the thresholdtDist are considered to be valid. Finally, bounding boxes ofregions are calculated to check the similarity between regionsand rectangles since ICs are always rectangular or quasi-rectangular objects. Segmented ICs are marked in Fig. 8(c)with green borders. Despite the straightforward segmentationapproach, all target objects are successfully identified.

V. EVALUATION AND DISCUSSION

In analogy to assessing the performance of binary clas-sification test, some statistical measures were employed for

the evaluation of segmentation results. In PCB images, all tobe segmented target objects (SMDs) are true positive (TP ).The segmented objects which do not belong to target objects(artifacts, bounding borders without devices, etc.) are falsepositive (FP ). All not segmented target objects are falsenegative (FN ). For segmentation, there is no meaningful truenegative (TN ) which can be specified. Then, the sensitivity Sand the precision P of segmentation are defined as

S =#(TP )

#(TP ) + #(FN)and P =

#(TP )

#(TP ) + #(FP ).

To analyze the affect of imaging conditions on segmenta-tion, images of test PCBs were acquired for different spatialresolutions (13 ∼ 28 pixels/mm) and under different illumi-nations (dome and lateral). In Fig. 9, two images of the sameboard region but of different spatial resolutions are presentedrespectively. Labels on devices, solder joints and assemblyprint are blurred in the image of lower spatial resolution. InFig. 10, the constructions of the used lateral and dome illumi-nations are sketched out. Two exemplar images of the sameboard region but under the two illuminations are also presentedrespectively. Under the dome illumination, the objects on thecircuit board are more visible. For example, the solder jointsare completely differentiable from the background.

The performance was at first evaluated using all images.For the segmentation of small devices, there were totally3196 target objects available. Using the assembly print-basedapproach and subsequently the hierarchical assessment, 87.2%of the searched objects (2788 of 3196) were successfullyidentified: S = 0.872; 78.6% of the segmented objects(2788 of 3546) were truly searched targets: P = 0.786.For the segmentation of ICs, there were totally 128 targetobjects available. Using the RAD-based approach, 81.2% ofthe searched objects (104 of 128) were successfully identified:S = 0.812; 68.0% of the segmented objects (104 of 153)were truly searched targets: P = 0.680. The performance ofsegmentation was then assessed using images of correspondingimaging conditions respectively. A summary of S and P forevaluated factors is available in Tab. 1. Obviously, there wasalways a trade-off between attainable sensitivity and precision.

The overall segmentation results were satisfying as the mostSMDs were successfully segmented and only a minor part ofthe segmented objects was FP . The presented segmentation ofSMDs is part of the whole information retrieving process foran automated PCB recycling. Further information retrievingoperations, such as recognition of SMDs, are to be carried outon the data from currently obtained segmentation results. Inconsideration of this, a higher sensitivity is preferable over ahigher precision since FP can be rejected later during furtheranalysis. Referring to the evaluation results in Tab. 1, thedome illumination should be chosen instead of the laterallypositioned variant so that improved segmentation performancecan be achieved (S = 0.918). No significant variations ofS and P were observed for different spatial resolutions. Thelower resolution (minimal 13 pixels/mm) is then preferred forthe computational efficiency.

(a) 14 pixels/mm (b) 28 pixels/mm

Fig. 9. Images of different spatial resolutions

TABLE IEVALUATION OF IMAGING CONDITIONS

target measure of illumination resolution

objects performance (pixels/mm)

lateral dome < 17 > 17

small sensitivity S 0.786 0.918 0.889 0.901

devices precision P 0.883 0.746 0.752 0.768

ICs sensitivity S 0.816 0.811 0.811 0.811

precision P 0.689 0.676 0.662 0.698

VI. CONCLUSION

For achieving an efficient recycling of waste PCBs, it ismost important to retrieve the information about materialcomposition, material distribution and properties of materialcarriers (electronic devices and other components). This en-ables subsequently an optimization of the recycling process.To this end, as part of a comprehensive analysis of PCBs,the segmentation of SMDs was presented in this paper. Asconfirmed by the evaluation, satisfying segmentation resultsfor further analysis steps were obtained.

In contrast to the mostly employed approaches based ondevice templates and solder joints, our approach is more flex-ible and reliable with respect to the variability of devices and

(a) Lateral illumination (b) Dome illumination

(c) PCB under L-illumination (d) PCB under D-illumination

Fig. 10. Illuminations: (a) and (b) illustrate the constructions of lateral anddome illuminations. The two images presented in (c) and (d) were taken forthe same PCB but under the lateral and the dome illumination respectively.

erosion or soiling of solder joints. The approach was originallydeveloped under context of PCB recycling. However, it shouldnot be restricted to only this application. As a demonstrativeexample, the performance of AOI for PCBs could be improvedby integrating this approach into automated inspections.

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