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dc.contributor.authorGonzales, Mark Edward M.
dc.contributor.authorUy, Lorene C.
dc.contributor.authorIlao, Joel P.
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-17T14:20:16Z
dc.date.available2023-10-17T14:20:16Z
dc.date.issued2023
dc.identifier.citationWSCG 2023: full papers proceedings: 1. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 107-116.en
dc.identifier.isbn978-80-86943-32-9
dc.identifier.issn2464–4617 (print)
dc.identifier.issn2464–4625 (CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/54416
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectzrcadlová segmentacecs
dc.subjectdetekce objektucs
dc.subjectkonvoluční neuronové sítěcs
dc.subjectprořezávání CNN filtrucs
dc.titleDesigning a Lightweight Edge-Guided Convolutional Neural Network for Segmenting Mirrors and Reflective Surfacesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe detection of mirrors is a challenging task due to their lack of a distinctive appearance and the visual similarity of reflections with their surroundings. While existing systems have achieved some success in mirror segmentation, the design of lightweight models remains unexplored, and datasets are mostly limited to clear mirrors in indoor scenes. In this paper, we propose a new dataset consisting of 454 images of outdoor mirrors and reflective surfaces. We also present a lightweight edge-guided convolutional neural network based on PMDNet. Our model uses EfficientNetV2-Medium as its backbone and employs parallel convolutional layers and a lightweight convolutional block attention module to capture both low-level and high-level features for edge extraction. It registered Fβ scores of 0.8483, 0.8117, and 0.8388 on the Mirror Segmentation Dataset (MSD), Progressive Mirror Detection (PMD) dataset, and our proposed dataset, respectively. Applying filter pruning via geometric median resulted in Fβ scores of 0.8498, 0.7902, and 0.8456, respectively, performing competitively with the state-of-the-art PMDNet but with 78.20× fewer floating-point operations per second and 238.16× fewer parameters. The code and dataset are available at https://github.com/memgonzales/mirror-segmentation.en
dc.subject.translatedmirror segmentationen
dc.subject.translatedobject detectionen
dc.subject.translatedconvolutional neural networken
dc.subject.translatedCNN filter pruningen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3301.14
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2023: Full Papers Proceedings

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