MARC details
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04099ntm a2200385 i 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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MY-KuUP |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251125110803.0 |
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t||||fr|||| 000 0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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ta |
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230821t20232023my a|||fr|||| 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Qualifying information |
hardback |
| International Standard Book Number |
THE0009687 (Local) |
| 039 ## - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
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| Level of rules in bibliographic description |
UMP |
| 040 ## - CATALOGING SOURCE |
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| Language of cataloging |
eng |
| Transcribing agency |
UMP |
| Description conventions |
rda |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
FTKPM .T36 2023 r Thesis |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Tang Jin Cheng |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
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| Title |
Vision-based human presence detection pipeline by means of transfer learning approach / |
| Statement of responsibility, etc. |
Tang Jin Cheng |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
Kuantan, Pahang : |
| Name of producer, publisher, distributor, manufacturer |
UMP, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2023 |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Date of production, publication, distribution, manufacture, or copyright notice |
©2023 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xiv, 123 pages : |
| Other physical details |
illustrations (some color) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1-CD ROM |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 336 ## - CONTENT TYPE |
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| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
unmediated |
| 337 ## - MEDIA TYPE |
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| Source |
rdamedia |
| Media type term |
computer |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
volume |
| 338 ## - CARRIER TYPE |
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| Source |
rdacarrier |
| Carrier type term |
computer disc |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
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| Source |
rda |
| File type |
text file |
| Encoding format |
PDF |
| 500 ## - GENERAL NOTE |
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| General note |
Faculty of Manufacturing and Mechatronic Engineering Technology |
| 502 ## - DISSERTATION NOTE |
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| Dissertation note |
Thesis (Master of Science) -- Universiti Malaysia Pahang – 2023 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Over the last century, industrial robots have gained an immense amount of popularity in replacing the human workers due to their highly repetitive nature. It was a twist to the industries when the concept of cooperative robots, known as cobots, has been innovated. Sharing space between the cobots and human workers has considered as the most effective way of utilizing the cobots. Keeping in mind that the safety of the human workers is always the top priority of the cobot applications in the industries, many time and efforts have been invested to improve the safeness of the cobots deployments. Yet, the utilization of deep learning technologies is rarely found in accordance with human detection in the field of research, especially the transfer learning approach, providing that the visual perception has shown to be a unique sense that still cannot be replaced by other. Hence, this thesis aimed to leverage the transfer learning approach to fine-tune the deep learning-based object detection models in the human detection task. In relation to this main goal, the objectives of the study were as follows: establish an image dataset for cobot environment from the surveillance cameras in TT Vision Holdings Berhad, formulate deep learning-based object detection models by using the transfer learning approach, and evaluate the performance of various transfer learning models in detecting the presence of human workers with relevant evaluation metrics. Image dataset has acquired from the surveillance system of TT Vision Holdings Berhad and annotated accordingly. The variations of the dataset have been considered thoroughly to ensure the models can be well-trained on the distinct features of the human workers. TensorFlow Object Detection API was used in the study to perform the fine-tuning of the one-stage object detectors. Among all the transfer learning strategies, fine-tuning has chosen since it suits the study well after the interpretation on the size-similarity matrix. A total of four EfficientDet models, two SSD models, three RetinaNet models, and four CenterNet models were deployed in the present work. As a result, SSD-MobileNetV2-FPN model has achieved 81.1% AP with 32.82 FPS, which is proposed as the best well-balanced fine-tuned model between accuracy and speed. In other case where the consideration is taken solely on either accuracy or inference speed, SSD_MobileNetV1-FPN model has attained 87.2% AP with 28.28 FPS and CenterNet-ResNet50-V1-FPN has achieved 78.0% AP with 46.52 FPS, which is proposed to be the model with best accuracy and inference speed, respectively. As a whole, it could be deduced that the transfer learning models can handle the human detection task well via the fine-tuning on the COCO-pretrained weights. |
| 610 20 - SUBJECT ADDED ENTRY--CORPORATE NAME |
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| Corporate name or jurisdiction name as entry element |
Faculty of Manufacturing and Mechatronic Engineering Technology |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Universities and colleges |
| General subdivision |
Dissertations |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Theses |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Thesis |