03018ntm a2200337 i 4500 MY-KuUP 20260306125345.0 a||||fr|||| 000 0 ta 260306t20252025my a|||fr|||| 000 0 eng d THE0010425 (Local) Hardback UMPSA eng UMPSA rda FTKPM .Y66 2025 r Thesis Ericsson, Yong, author. Lidar object perception and classification using 3d machine learning algorithm / Ericsson Yong Kuantan, Pahang : UMPSA, © 2025 © 2025 xvi, 110 pages : illustrations ; 30 cm. + 1 CD-COM. rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Faculty of Manufacturing and Mechatronic Engineering Technology Thesis (Master of Science) -- Universiti Malaysia Pahang - 2025 Includes bibliographical references 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. Faculty of Manufacturing and Mechatronic Engineering Technology Dissertations Universities and colleges Dissertations Theses Dissertations lcc THESIS 0 0 lcc 0 1 20000 20000 2026-03-06 0 FTKPM .Y66 2025 r Thesis T000004800 2026-03-06 12:54:20 2026-03-06 THESIS 0 0 lcc 0 -2 20000 20000 2026-03-06 0 CD14001 T000004801 2026-03-06 12:54:48 2026-03-06 THESIS 104277 104283