000			03018ntm a2200337 i 4500
003			MY-KuUP
005			20260306125345.0
006			a||||fr|||| 000 0
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008			260306t20252025my a|||fr|||| 000 0 eng d
020			_aTHE0010425 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aFTKPM .Y66 2025 r Thesis
100	1		_aEricsson, Yong, _eauthor.
245	1	0	_aLidar object perception and classification using 3d machine learning algorithm / _cEricsson Yong
264		1	_aKuantan, Pahang : _bUMPSA, _c© 2025
264		4	_c© 2025
300			_axvi, 110 pages : _billustrations ; _c30 cm. + _e1 CD-COM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aFaculty of Manufacturing and Mechatronic Engineering Technology
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang - 2025
504			_aIncludes bibliographical references
520	3		_aAutonomous 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	2	0	_aFaculty of Manufacturing and Mechatronic Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses _xDissertations
942			_2lcc _cTHESIS
999			_c104277 _d104283