04378ntm a2200409 i 4500952013600000952011900136999001700255003000800272005001700280006001900297007000300316008004100319020003300360039000800393040001800401090002900419100002800448245010800476264003400584264001100618300007100629336002100700336002100721337002500742337002300767338002300790338003000813347002400843500006800867502007000935504004001005520276801045610008303813650004503896650001103941942001603952  00102lcc40718REFa20000b20000d2023-08-21l0oFTKPM .T36 2023 r ThesispT000002465r2023-08-21 00:00:00t1w2023-08-21yTHESIS  00102lcc40708REFa20000b20000d2023-08-21l0oCD13401pT000002466r2023-08-21 00:00:00t1w2023-08-21yTHESIS  c99912d99918MY-KuUP20251125110803.0t||||fr|||| 000 0 ta230821t20232023my a|||fr|||| 000 0 eng d  qhardbackaTHE0009687 (Local)  aUMP  bengcUMPerda  aFTKPM .T36 2023 r Thesis1 aTang Jin Chengeauthor.10aVision-based human presence detection pipeline by means of transfer learning approach /cTang Jin Cheng 1aKuantan, Pahang :bUMP,c2023 4c©2023  axiv, 123 pages :billustrations (some color) ;c30 cm. +e1-CD ROM  2rdacontentatext  2rdacontentatext  2rdamediaaunmediated  2rdamediaacomputer  2rdacarrieravolume  2rdacarrieracomputer disc  2rdaatext filebPDF  aFaculty of Manufacturing and Mechatronic Engineering Technology  aThesis (Master of Science) -- Universiti Malaysia Pahang – 2023  aIncludes bibliographical references3 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.20aFaculty of Manufacturing and Mechatronic Engineering TechnologyxDissertations 0aUniversities and collegesxDissertations 0aTheses  2lcccTHESIS