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AdvisorKönings, Bastiandc.contributor.advisor
AdvisorWeber, Fabiandc.contributor.advisor
AdvisorEhret, Heikodc.contributor.advisor
AuthorGünther, Marcodc.contributor.author
Date of accession2021-12-02T16:04:08Zdc.date.accessioned
Available in OPARU since2021-12-02T16:04:08Zdc.date.available
Year of creation2020dc.date.created
Date of first publication2021-12-02dc.date.issued
AbstractSophisticated object detection is used in automated systems throughout many areas of application. This work elaborates the possibilities of utilizing a modern object detection approach for visual analysis of printed circuit boards (PCB) in the context of hardware-related security assessments. In detail, the task is to reliably localize and classify different types of integrated circuits and other components on image data depicting electronic devices. Faster R-CNN, an object detection architecture based on modern convolutional neural networks, is selected for this purpose. In order to effectively train the detector a variety of datasets are collated that contain suitable annotations for PCB com- ponents. In parallel, an internal image acquisition process further complements the available datasets. All dataset are extended by introducing six new sub-categories for integrated circuits. This approach of IC class breakdown is unique and not present in any of the investigated datasets so far. In addition, four new categories for passive and miscellaneous components are also introduced into the data. Finally, the object detection architecture is appropriately configured and several models are trained with different combinations of the available image data. The best model yields an overall detection performance of 0.57 mAP. Detection scores of individual classes tend to be more vulnerable to small component sizes and high intra-class variance than to class imbalances of the datasets. Acceptable performance scores above 0.6 AP are reported for four out of ten classes, two even reach 0.75 AP.dc.description.abstract
Languageen_USdc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseCC BY 4.0 Internationaldc.rights
Link to license texthttps://creativecommons.org/licenses/by/4.0/dc.rights.uri
KeywordLabelingdc.subject
KeywordAnnotationsdc.subject
KeywordIT-Securitydc.subject
KeywordMachine learningdc.subject
KeywordObject detectiondc.subject
KeywordPrinted circuit boardsdc.subject
KeywordImage datasetsdc.subject
KeywordEmbedded hardwaredc.subject
Dewey Decimal GroupDDC 620 / Engineering & allied operationsdc.subject.ddc
LCSHMachine learningdc.subject.lcsh
LCSHComputer visiondc.subject.lcsh
LCSHLabelsdc.subject.lcsh
TitleVisual analysis of printed circuit boardsdc.title
Resource typeAbschlussarbeit (Bachelor)dc.type
Date of acceptance2020dcterms.dateAccepted
RefereeNeumann, Heikodc.contributor.referee
DOIhttp://dx.doi.org/10.18725/OPARU-40107dc.identifier.doi
PPN1782327347dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-40183-8dc.identifier.urn
GNDMaschinelles Lernendc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Neuroinformatikuulm.affiliationSpecific
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
In cooperation withSCHUTZWERK GmbHuulm.cooperation
Bibliographyuulmuulm.bibliographie
Project uulmSecForCARs / Verbundprojekt: Security for Connected, Autonomous caRs - SecForCARs -; Teilvorhaben: Sichere Verarbeitungskette / BMBF / 16KIS0797uulm.projectOther


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