MARC details
| 000 -LEADER |
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| fixed length control field |
03018ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20260306125345.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
a||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
260306t20252025my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010425 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKPM .Y66 2025 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Ericsson, Yong, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Lidar object perception and classification using 3d machine learning algorithm / |
| Statement of responsibility, etc. |
Ericsson Yong |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2025 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 110 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 CD-COM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Faculty of Manufacturing and Mechatronic Engineering Technology |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang - 2025 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
Autonomous vehicles (AVs) are emerging technologies aimed at reducing the rate of ever-increasing traffic accidents at Malaysia involving heavy vehicles. One of the key technologies in detecting obstacles during autonomous vehicle navigation is 3D LiDAR sensor which captures sparse and disordered point clouds making identifying objects inside it challenging. To address this, deep learning has been utilized in point clouds and now it can be represented in several forms like pillar, voxel or point form for feature extraction in object detection. In this research, 3D LiDAR data of heavy vehicles are collected using both a stationary setup (static) and a mobile platform (dynamic) with a sensor mounted on top of a moving vehicle within a university campus. Then, 3D object detectors were pretrained using transfer learning with the addition of ‘Van’ and ‘Truck’ class. Also, their anchor dimensions are based on the most frequently used van and truck models in Malaysia. The 3D object detectors selected are PointPillars, PartA2Free and SECOND to represent the pillar-form and the voxel form respectively. Upon pretraining them, there’s are changes in mAP (mean Average Precision) scores for the 3D object detectors, especially for the Van and Truck class. The same pretrained 3D object detectors are then deployed onto point cloud scenes and are compared between the original detectors in terms of classification score. It was observed that the pretrained 3D object detectors had an improvement on recall and a reduction in False Negative detections, confirming that transfer learning enhances detection of objects. These findings prove that the dataset collected for transfer learning is feasible to improve object or vehicle recognition in Malaysian Autonomous Vehicles applications. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Faculty of Manufacturing and Mechatronic Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Theses |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |