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Abstract Header

Object Detection and Classification Based on Point Separation Distance Features of Point Cloud Data

Ji, Jiajie
http://orcid.org/0009-0001-9879-4587
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1689654675688913

Abstract Details

2023, Master of Science (M.S.), University of Dayton, Electro-Optics.
Today, with the development of artificial intelligence and autonomous driving in full swing, lidar is playing a vital role. As an important sensing and detection component, lidar uses 3D point cloud images as a medium to allow artificial intelligence systems to perceive the outside world and perform reasoning work. Therefore, the processing and operation implementation of point cloud is an important part of the information processing of a lidar system, which will determine the accuracy and feasibility of artificial intelligence judgment. In this thesis, an analysis method based on extracting point cloud point separation distance distribution features is used. First, we will introduce how a lidar system works and how a lidar system collects information and generates a 3D point cloud. Afterward, feature analysis of point cloud point separation distribution for dimensionality reduction will be proposed. At the same time, we will use the point separation distribution feature to do object classification, object recognition and segmentation of whether there are vehicles on the road. What's more worth mentioning is that we also provide deep learning results and analysis based on point cloud point separation distribution features. On this basis, we discuss the significance and practicality of this feature analysis.
Edward Watson (Committee Chair)
Partha Banerjee (Committee Member)
Miranda van Iersel (Committee Member)
61 p.
Electrical Engineering; Engineering; Optics
point cloud; point separation distance features; Lidar; object detection and classification; deep learning

Recommended Citations

Citations

  • Ji, J. (2023). Object Detection and Classification Based on Point Separation Distance Features of Point Cloud Data [Master's thesis, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1689654675688913

    APA Style (7th edition)

  • Ji, Jiajie. Object Detection and Classification Based on Point Separation Distance Features of Point Cloud Data. 2023. University of Dayton, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1689654675688913.

    MLA Style (8th edition)

  • Ji, Jiajie. "Object Detection and Classification Based on Point Separation Distance Features of Point Cloud Data." Master's thesis, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1689654675688913

    Chicago Manual of Style (17th edition)

dayton1689654675688913
84
© 2023, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.