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Auto object recognition classification based on unmanned aerial vehicle (UAV)/drone orthophoto using fast RCNN / Aiman Fithri Bin Tharmizi

By:

Aiman Fithri Bin Tharmizi [author.]

Material type: Text

text

Media type:

unmediated

Carrier type:

volume

ISBN:

THE0010042 (Local)

Subject(s):

Dissertation note: Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 Abstract: 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.

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Title notes ( 4 )

Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Final Year Report	UMPLIB GAMBANG	CD13589 (Browse shelf(Opens below))	In Transit		T000003187

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.