000			03203ntm a2200337 i 4500
999			_c102508 _d102514
003			MY-KuUP
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008			250509t20232023my a|||fs|||| 000 0 eng d
020			_aTHE0010042 (Local) _qHardback
040			_aUMPSA _beng _cUMPSA _erda
090			_aPSM .A36 2023 r Bc
100	1		_aAiman Fithri Bin Tharmizi, _eauthor.
245	1	0	_aAuto object recognition classification based on unmanned aerial vehicle (UAV)/drone orthophoto using fast RCNN / _cAiman Fithri Bin Tharmizi
264		1	_aKuantan Pahang: _bUMPSA, _c2023
264		4	_c© 2023
300			_axi, 36 pages : _billustration ; _e1 CD-ROM.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
347			_2rda _atext file _bPDF
500			_aCenter for Mathematical Sciences
502			_aBachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aMalaysia has seen an increase in the number of strange or random objects on its roads in recent years. The increasing use of unmanned aerial vehicles (UAVs) for aerial imaging has led to the availability of large amounts of aerial imagery data. This data can be used for various applications, including object recognition and classification. In this research, we propose an "Auto Object Recognition Classification Based on Unmanned Aerial Vehicle (UAV)/Drone Orthophoto Using Fast RCNN" to automate the process of object recognition and classification from UAV/drone orthophotos. Therefore, this problem has caused many problems for road users, such as traffic, road damage, and accidents. Some businesses still use manual object recognition, which takes time and effort. The objective of this project is to analyse the orthophoto and generate a classification of object detection by achieving an accuracy of 50% or above and faster than manual digitization of the object. Convolutional neural network (CNN) techniques will be used to train the system. MySpatial Sdn Bhd will provide the dataset by detecting the object using object detection classification using drone orthophoto. The proposed algorithm will use deep learning techniques and computer vision methods to extract relevant features from the UAV/drone orthophotos and classify objects into different categories. The algorithm will be implemented using Python and evaluated based on its accuracy, efficiency, and scalability. The results of this research will contribute to the development of more efficient and effective object recognition and classification algorithms for UAV/drone orthophotos. The findings of this research will be useful for various applications, including urban planning, environmental monitoring, and disaster management. The proposed algorithm has the potential to provide accurate and timely information for decision-making and resource allocation in these domains.
610	2	0	_aCenter for Mathematical Sciences _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aFinal Year Report _xDissertations
942			_2lcc _cPSM