Eye Landmarks Detection using RT-DETR with Rules

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dc.contributor.authorBoonnithititikul, Chatree
dc.contributor.authorJaknamon, Teetouch
dc.contributor.authorChawuthai, Rathachai
dc.date.accessioned2026-08-06T10:44:05Z
dc.date.available2026-08-06T10:44:05Z
dc.date.issued2024-01-01
dc.description.abstractIn order to help ophthalmologists diagnose eye problems, it is necessary to scan for eye landmarks such as the pupil, the reflection point on the retina, and the boundary of the eye. An individual's eye landmarks on their face can be obtained via some facial landmarks' detection methods, including Haar Cascade. Two problematic aspects of the current approaches, however, are that the pupil and reflection point information is not provided, and the detection is ineffective when confronted with a picture of the upper half of the face or a person wearing a mask. In this study, we intend to develop a deep learning model for eye landmark identification using the Realtime identification Transformer (RT-DETR) approach together with our rules. As a consequence, nine landmark points-two for the eye, six for the pupil, and one for the reflection, are computed with an accuracy of 0.974. Since the focus of this paper is on eye landmark recognition, the next stage will be to build an application and a machine learning model for the diagnosis of eye disorders. - Keywords Deep Learning, Detection, Eye Landmarks, Facial Landmarks, Ophthalmology, RT-DETR
dc.identifier.citation2024 21st International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2024, 2024
dc.identifier.doi10.1109/ECTI-CON60892.2024.10594871
dc.identifier.other2-s2.0-85201162109
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15104
dc.source2024 21st International Conference on Electrical Engineering Electronics Computer Telecommunications and Information Technology Ecti Con 2024
dc.subjectDeep Learning
dc.subjectDetection
dc.subjectEye Landmarks
dc.subjectFacial Landmarks
dc.subjectOphthalmology
dc.subjectRT-DETR
dc.titleEye Landmarks Detection using RT-DETR with Rules
dc.typeConference Paper

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