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dc.contributor.authorAnthony, Benedicten_US
dc.identifier.citationAnthony, B. (2022). Performance analysis of object detection algorithms using small training datasets. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractObject detection using machine learning approach has seen wide adoption in virtually all known industries in the past decade. Much investment and research has been put into building the most accurate object detection algorithm. However, implementation of these algorithms is only accessible to organizations with vast amount of computing and data procurement resources. In this study, the correlation of overall detection rate, training time and training sample size will be explored. In addition, threshold for minimum effective training sample size will be investigated in order to aid implementation of object detection in environments where annotated training samples are difficult to obtain. The experiment revealed that models trained using the LBP feature type performed significantly better in the 50-100 sample size range in terms of effectiveness compared to the HAAR feature type.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Materials::Material testing and characterizationen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titlePerformance analysis of object detection algorithms using small training datasetsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorKedar Hippalgaonkaren_US
dc.contributor.schoolSchool of Materials Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Materials Engineering)en_US
dc.contributor.organizationInstitute of Materials Research and Engineering, A*STARen_US
dc.contributor.supervisor2Jayce Cheng Jian Weien_US
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Appears in Collections:MSE Student Reports (FYP/IA/PA/PI)
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