MARC details
| 000 -LEADER |
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| fixed length control field |
03203ntm a2200337 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251125111038.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
t||||fo|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
ta |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250509t20232023my a|||fs|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
THE0010042 (Local) |
| Qualifying information |
Hardback |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
UMPSA |
| Language of cataloging |
eng |
| Transcribing agency |
UMPSA |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
PSM .A36 2023 r Bc |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Aiman Fithri Bin Tharmizi, |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Auto object recognition classification based on unmanned aerial vehicle (UAV)/drone orthophoto using fast RCNN / |
| Statement of responsibility, etc. |
Aiman Fithri Bin Tharmizi |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Kuantan Pahang: |
| Name of producer, publisher, distributor, manufacturer |
UMPSA, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
© 2023 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xi, 36 pages : |
| Other physical details |
illustration ; |
| Accompanying material |
1 CD-ROM. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type term |
volume |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Center for Mathematical Sciences |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Bachelor of Applied Science In Data Analytics With Honour -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
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. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
|---|
| Corporate name or jurisdiction name as entry element |
Center for Mathematical Sciences |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Final Year Report |
| General subdivision |
Dissertations |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Final Year Report |