dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Poppe, Ronald | |
dc.contributor.author | Veroni, Eleni | |
dc.date.accessioned | 2024-02-15T14:50:00Z | |
dc.date.available | 2024-02-15T14:50:00Z | |
dc.date.issued | 2024 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/45943 | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Face detection in complex environments, particularly in the context of monitoring preterm infants, presents unique challenges due to occlusions such as hats or medical equipment, variation in lighting, video quality etc. Standard face detection models often fall short in such scenarios. Addressing the occlusion problem, this thesis proposes a novel two-part YOLOv5 model approach. The first part includes training the model on a customized dataset with bounding boxes of different facial parts. The | |
dc.title | Uncovering the Invisible: Improving Face Detection Under Occlusions | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | face detection, preterm infants, AI, CNN, YOLO, YOLOv5, explainability, gradcam, hirescam | |
dc.subject.courseuu | Artificial Intelligence | |
dc.thesis.id | 23035 | |