000			04099ntm a2200385 i 4500
999			_c99912 _d99918
003			MY-KuUP
005			20251125110803.0
006			t||||fr|||| 000 0
007			ta
008			230821t20232023my a|||fr|||| 000 0 eng d
020			_qhardback _aTHE0009687 (Local)
039			_aUMP
040			_beng _cUMP _erda
090			_aFTKPM .T36 2023 r Thesis
100	1		_aTang Jin Cheng _eauthor.
245	1	0	_aVision-based human presence detection pipeline by means of transfer learning approach / _cTang Jin Cheng
264		1	_aKuantan, Pahang : _bUMP, _c2023
264		4	_c©2023
300			_axiv, 123 pages : _billustrations (some color) ; _c30 cm. + _e1-CD ROM
336			_2rdacontent _atext
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
337			_2rdamedia _acomputer
338			_2rdacarrier _avolume
338			_2rdacarrier _acomputer disc
347			_2rda _atext file _bPDF
500			_aFaculty of Manufacturing and Mechatronic Engineering Technology
502			_aThesis (Master of Science) -- Universiti Malaysia Pahang – 2023
504			_aIncludes bibliographical references
520	3		_aOver 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	2	0	_aFaculty of Manufacturing and Mechatronic Engineering Technology _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses
942			_2lcc _cTHESIS