03203ntm a2200337 i 4500 0 0 lcc 0 6 10000 10000 2025-05-09 0 CD13589 T000003187 2025-05-09 00:00:00 2025-05-09 PSM 102508 102514 MY-KuUP 20251125111038.0 t||||fo|||| 000 0 ta 250509t20232023my a|||fs|||| 000 0 eng d THE0010042 (Local) Hardback UMPSA eng UMPSA rda PSM .A36 2023 r Bc Aiman Fithri Bin Tharmizi, author. Auto object recognition classification based on unmanned aerial vehicle (UAV)/drone orthophoto using fast RCNN / Aiman Fithri Bin Tharmizi Kuantan Pahang: UMPSA, 2023 © 2023 xi, 36 pages : illustration ; 1 CD-ROM. rdacontent text rdamedia unmediated rdacarrier volume rda text file PDF Center for Mathematical Sciences Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 Includes bibliographical references Malaysia 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. Center for Mathematical Sciences Dissertations Universities and colleges Dissertations Final Year Report Dissertations lcc PSM