Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/157247
Title: Performance analysis of object detection algorithms using small training datasets
Authors: Anthony, Benedict
Keywords: Engineering::Materials::Material testing and characterization
Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Anthony, B. (2022). Performance analysis of object detection algorithms using small training datasets. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/157247
Abstract: Object 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.
URI: https://hdl.handle.net/10356/157247
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MSE Student Reports (FYP/IA/PA/PI)

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